Research degrees with the School of Computing and Engineering

Primary supervisor: Dr Laden Husamaldin

Start date: January, May or September of each academic year

Duration: 3 years full-time or 5 years (part-time)

Research context

Project management is the key for driving business in many organisations. With the advancement of technologies, projects are becoming more complicated and the decision-making process becomes sophisticated. Analytics depicts how a project relates to and creates an influence on the entire organisation. With the aid of project management analytics, project management teams can determine whether the project task will be completed on time and as per client specifications. However, research suggested that poor understanding of analytics is leading to ineffectiveness when it comes to linking the project management methodology’s values and principles to the goals of adapting analytics in a project. Moreover, there is a lack in assessing what value does good analytics bring for project management.

Research goals

This research aims to develop an analytical project management model. The model will support what analytics means for project management and how it can benefit the delivery of successful projects.

Candidate profile

The ideal candidate should have an MSc or equivalent degree in relevant subjects. The potential candidate should be able to work in a collaborative environment with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with artefact development with real data and critically evaluate the proposed solution through case studies.

Background knowledge and/or previous experience in the following areas, though not mandatory, will be considered very favourably: innovation, project management, information systems and analytics.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Laden Husamaldin: laden.husamaldin2@uwl.ac.uk

Supervisory team: Dr Nasrullah Khilji and Dr Laden Husamaldin

Start date: January, May or September of each academic year

Duration: 3 years (full-time) or 5 years (part time)

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This particular proposal focuses on open innovation and value creation in knowledge-intensive business services (KIBS) organisations.

Organisations create and deliver value to customers through a variety of ways, such as offering tangible goods or intangible services. In the modern world, more and more organisations deliver intangible services to their customers. Organisations that offer services often utilise knowledge to create value and contribute to the knowledge economy. Knowledge-intensive business services (KIBS) organisations accumulate, create and disseminate supplier’s specialist knowledge to deliver customised services or solutions to satisfy customer needs. Based on the nature of their offerings, organisations might create value via a range of value creation logics, eg chain logic, shop logic, web logic, network logic and package logic.

Open innovation has changed the new product development (NPD) process for many organisations. Apart from product-based organisations, service providers have adopted open innovation. In recent years, KIBS firms have been under pressure to innovate in order to meet modern world customers’ requirements. Whilst many researchers investigated into open innovation in KIBS organisations, the main focus has been on large organisations. SMEs normally face more resource constraints than large organisations, and this could potentially influence the adoption of open innovation in SMEs. In order to create a more enabling environment for open innovation in KIBS SMEs, it is necessary to gain further insights into this domain.

Research goal

The main goal of this research is to understand how to create an environment that facilitates open innovation in KIBS SMEs. In order to do so, the following key objectives need to be met.

• To identify the factors and barriers to open innovation in KIBS SMEs
• To clarify the correlation between open innovation factors and different value creation logics in KIBS SMEs
• To develop a framework to facilitate and manage open innovation in KIBS SMEs

Candidate profile

The ideal candidate should have an MSc or equivalent degree in relevant subjects. A strong commitment to reaching research excellence and achieving assigned objectives is required, so has an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with workplace studies to explore the research goals, and conclude with the validation of a proposed framework.

Background knowledge and/or previous experience in the following areas, though not mandatory, will be considered very favourably: innovation, business process management, information systems and professional services (eg legal services, accounting, R&D and management consultancy).

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

NB: This PhD proposal guide is aimed at helping the potential candidate to write and submit a good research proposal. It is intended here to help you to think about your proposed PhD research in a clear, structured, and meaningful way.

Further information

Questions regarding academic aspects of the project should be directed to Dr Nasrullah Khilji: nasrullah.khilji@uwl.ac.uk

Primary supervisor: Dr Nasrullah Khilji

Start dates: January, May and September of each academic year

Duration: 3 years (full-time) or 5 years (part-time)

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This project management PhD programme based in the School of Computing and Engineering specifically focuses on project management practices. Student will study the nature of project management activities such as leadership, team dynamics, innovation, risk, quality, stakeholder management, contract, procurement, conflict and dispute resolution, marketing and project financing.

The University of West London (UWL) welcomes MPhil / PhD proposals from outstanding applicants related to project management research topics. This research degree at UWL basically aims to make an original contribution to knowledge leading to the enhancement of academia and practice. The PhD candidates at UWL have previously secured academic positions in various leading universities and key roles as leading experts in leading enterprises both in public and private organisations.

This research degree programme intends to prepare PhD students with vibrant project management expertise in diverse areas such as technology innovation, big data analytics, business intelligence, infrastructure development, economic growth and sustainable development, management of project enterprises and project-based business systems. The research programme covers topics for enhanced skills, knowledge, tools and techniques needed to effectively lead volatile, uncertain and ambiguous projects from inception to completion in global markets.

Research goal

• The research focuses project management including innovation, leadership, team dynamics, sustainability, project strategy and risk management
• Project financing, infrastructure development, economics and finance related to construction, business platforms and IT projects
• Data science including big data analytics, business intelligence, management information system, social networks, manufacturing services

Candidate profile

The ideal candidate should have an MSc or equivalent degree in relevant subjects. Strong commitment to reaching research excellence and achieving assigned objectives is required, so has an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with workplace studies to explore the research goals and conclude with the validation of a proposed framework.

Background knowledge and/or previous experience in the following areas, though not mandatory, will be considered very favourably: project and operations management, business processes and information management and professional services (eg legal services, accounting, R&D and management consultancy).

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

NB: This PhD proposal guide is aimed at helping potential candidate to write and submit a good research proposal. It is intended here to help you to think about your proposed PhD research in a clear, structured and meaningful way.

Further information

Questions regarding academic aspects of the project should be directed to Dr Nasrullah Khilji: nasrullah.khilji@uwl.ac.uk

Supervisory Team: Dr Nasrullah Khilji

Start Date: January, May or September of each academic year

Duration: 3 years (full-time) or 5 years (part time)

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This particular proposal focuses on soft skills acquisition by developing a research strategy based on innovation, big data analytics and knowledge management for economic growth in emerging economies as a case study.

Soft skills has become a popular and widely used term to designate a major component of the learned techniques and abilities (skills) which are necessary for human development and which are progressively acquired over a lifetime. Soft skills complement hard skills as the fundamental tool skills. Digital technologies are the principal drivers of economic growth worldwide and a catalyst for change and innovation within established patterns of economic activity. Education systems at every level respond to support economic activity as much as to develop the individual for immediate as well as for longer term learning needs. Digital literacy is necessary to provide future empowerment, building on functional literacy, numeracy and employability skills.

The nature of hard and soft skill domains and their related applications can be examined in a case study directed to specific developing countries environments. However, just as hard skills can be described and defined in functional and instrumental terms, a similar approach can be taken in this research project towards soft skills. The main focus of this research project will be both on generic soft skills as well as on their extension into the whole range of subject and context specific domains. The research work needs to look into a root case study defined by various features within the undertaken case study i.e. emergent economic structures and activities, digital technologies, big data analytics, knowledge management, innovation and digital transformation.

The research is required to carry out an investigation to identify the key factors in the acquisition of soft skills in an emergent intermediate economy. Exploiting the digital transformation and the knowledge economy requires new technical skills and competences (so-called ‘hard skills’) as well as a complex of ‘soft skills’ encompassing human behaviours, communication resources, social interaction, intellectual and cognitive processes, these call for qualities of analysis, problem-solving, creativity, planning and a multitude of managerial and operative functions. The research will lead to identify how in the digitally connected environment, the meshing together of essential skills that could lead towards economic growth and sustainable development.

Research goal

Technical and occupational skills continue to provide the base for economic capacity, but the knowledge economy requires new levels of personal and social abilities drawing on all aspects of human communication and emerging economies interaction based on innovation, big data analytics and knowledge management. In order to do so, the following key objectives need to be achieved by the end of this research project.

• To identify key factors in the acquisition of soft skills towards economic growth
• To formulate innovative approaches for economic system transformation
• To analyse the socio-economic development policies encompassing productivity in industrial sectors through ‘soft skills’ acquisition
• To develop a framework to facilitate and manage economic growth based on innovation, big data analytics and knowledge management

Candidate profile

The ideal candidate should have an MSc or equivalent degree in relevant subjects. A strong commitment to reaching research excellence and achieving assigned objectives is required, so has an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with workplace studies to explore the research goals and conclude with the validation of a proposed framework.

Background knowledge and/or previous experience in the following areas, though not mandatory, will be considered very favourably: innovation, business process management, information systems and professional services (eg legal services, accounting, R&D and management consultancy).

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

NB: This PhD proposal guide is aimed at helping the potential candidate to write and submit a good research proposal. It is intended here to help you to think about your proposed PhD research in a clear, structured and meaningful way.

Further information

Questions regarding academic aspects of the project should be directed to Dr Nasrullah Khilji: nasrullah.khilji@uwl.ac.uk

Primary supervisor: Professor Charlie Fu

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Research context

Computational architectural design, including parametric and generative design, is a state of art technology in architectural design industry today.

This research aims to develop creative, practical and integrated methods and techniques to implement the existing IT technologies and software (AutoCAD Revit, Dynamo, Rhino 6 with Grasshopper) to enable the teaching and practice of parametric and generative architectural design. This includes the development of formulas and packages of various shapes of buildings and spaces.

This research will also expand into sustainable and responsive architectural design with Micro-Control systems, such as Arduino with C# programming and various sensors, such as CO2, thermal control, noise control etc.

Research goal

This research aims to develop creative, practical and integrated methods and techniques by implementing the existing latest IT technologies and software (AutoCAD Revit, Dynamo, Rhino 6 with Grasshopper) to enhance the teaching and practice of parametric and generative architectural design.

Candidate profile

The applicant should have strong education background in any of the following areas, including built environment, computing or electronic/mechanic engineering and have good understanding of architectural design principles and techniques. They should also be able to use BIM-based CAD systems such as AutoCAD Revit, ArchiCAD or Rhino 6, etc, with experience in graph programming with Dynamo or Grasshoppers. The skills of C# or Python programming will be desirable but not essential.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

For general enquiries about the application process, visit the Graduate School pages.

Questions regarding academic aspects of the project should be directed to Professor Charlie Fu: Charlie.Fu@uwl.ac.uk

Primary supervisor: Dr Bolanle Noruwa 

Start dates: January, May and September of each academic year 

Duration: 3 years full-time or 5 years part-time

Research context

Maximum project performance is achieved only on some construction projects due to cost and time overrun. A new trend in construction innovation indicates that the construction industry now embraces modern technology in its day-to-day operations. Several continents worldwide are committed to promoting the adoption of technologies in the construction process and procedure through various strategies to improve project delivery and cost-effectiveness. 

Many nations are determined to transform their architecture, engineering and construction (AEC) industry. Apart from the use of building information modelling (BIM), drones, 3D scanning, virtual reality (VR), augmented reality (AR), mixed reality (MR) and others that are already in use for the delivery and management of projects, other technologies like artificial intelligence (AI), machine learning (ML), 3D printing, robotic equipment are emerging. As opposed to the slow and labour-intensive industry of the past, the industry is adopting technological advancement capable of making construction processes much faster and more efficient.

Research goal

This doctoral study investigates the application and benefits of these emerging technologies to improve cost and time certainty in construction projects at pre- and post-contract stages. The project will explore the place of the traditional approach to construction management and professional judgement in parallel to using technologies.

Candidate profile

The successful applicant should be capable of using various research methods: qualitative, quantitative, focus groups and case studies at organisational and operational levels. This research study is open to Home and International students. To be eligible, applicants should have a first class honours degree or 2.1 plus a Masters degree (or equivalent experience) in relevant fields. 

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at an overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

For general enquiries about the application process, visit the Graduate School pages.

For informal enquiries, please get in touch with Dr Bolanle Noruwa: bolanle.noruwa@uwl.ac.uk

Primary supervisor: Professor Charlie Fu

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Research context

CO2 levels of indoor environment are usually used as a key factor to indicate indoor air quality, although CO2 doesn’t directly harm people. Recently research and measurements show that CO2 levels in classrooms of many education buildings can change significantly (between 600ppm and over 3000ppm) in a couple of hour’s lessons. This issue can be more severe in wintertime in some cold areas as all windows have to be kept closed.

Previous researches also demonstrated that high indoor CO2 levels will affect people’s decision making effectiveness. This research specifically focuses on this issue by monitoring CO2, temperature, humidity and other indoor air quality figures in different classrooms, students groups and their performance and intends to discover the accurate relationship between students’ performance and indoor air quality. It also aims to develop some practical solutions to such issues and also monitoring the possible improvement related to students performance. This research is also related to the principles and methods of post-occupancy elevation (POE).

Research goal

This research aims to have a systemic study on the inter-relationship between classroom indoor air quality and students’ performance and explore the potential practical solutions to improve indoor air quality of classrooms. Through the programme, it will also expect to address a set of practical method and techniques to collect and analyse data for indoor air quality studies.

Candidate profile

The applicant should have a strong education background in any of the following areas, including built environment, electronic/mechanic engineering or computing, and have a good understanding of human comfort, indoor environment quality and general building technology. The applicant should also understand different data collection and analysis methods and techniques.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

For general enquiries about the application process, visit the Graduate School pages.

Questions regarding the academic aspects of the project should be directed to Professor Charlie Fu: Charlie.Fu@uwl.ac.uk

The following PhD subjects are offered under the supervision of Dr Fateme Dinmohammadi (fateme.dinmohammadi@uwl.ac.uk)

• Advanced algorithms for multimodal data fusion in sustainable environmental monitoring
• AI-based internet of things technologies
• Developing a machine learning-based digital twin infrastructure model for enhancing building energy efficiency
• Deep learning based real-time object detection on drone images
• Digital twins for smart cities and building information management
• Machine learning for the prediction of building energy consumption
• Smart water management using artificial intelligence and big data analytics
• Wearable sensors and e-textiles for healthcare

There are calls for four PhD projects within this topic:

1. Intelligent UAV-enabled Mobile Edge Computing for Dynamic Resource Allocation

Primary supervisor: Dr Shidrokh Goudarzi

Expected start date: January, May and September of each academic year   

Duration: This is a three-year position  

Research context

The wireless communication sector is amongst the fastest-growing segments of the communications industry, with the number of mobile subscriptions worldwide approaching the number of people on Earth. The increasing adoption of Mobile Nodes (MNs) is accelerating the progress of the Internet of Things (IoT) and the implementation of advanced mobile applications that require sophisticated capabilities like autonomous navigation and unmanned driving. 

Research goal

This study is driven by the idea of utilising unmanned aerial vehicle (UAV) as Mobile Edge Computing (MEC) servers in wireless networking. In this context, the research proposes a system that combines computation offloading and adaptive computing resource allocation, where UAVs act as edge servers to provide edge computing services to the MNs. The objective is to minimise the energy consumption of all MNs while ensuring effective task computation within a specific time frame. The optimisation of trade-offs between throughput, delay and energy consumption is achieved by designing an adaptive intelligent resource allocation (IRA) technique. 

Candidate profile

We are looking for highly motivated candidates with the following qualifications: 

• A Masters degree (or equivalent) in computer science, data science or a related field with a focus on AI algorithms, particularly machine learning and deep learning models

• Solid programming skills and experience in data processing and statistical analysis

• Strong analytical and problem-solving abilities

• Excellent communication and collaboration skills

• Demonstrated research potential through previous projects, publications or relevant work experience

• Familiarity with big data technologies and tools is advantageous

Further information
 
We encourage interested candidates to review the application requirements, including submission guidelines and important deadlines, on our website or by contacting Dr Goudarzi via email at Shidrokh.Goudarzi@uwl.ac.uk. Please include "PhD Application Inquiry - Intelligent UAV-enabled Mobile Edge Computing" in the subject line of your email for a prompt response.

2. Optimising Machine Learning for Embedded Systems

Primary supervisor: Dr Shidrokh Goudarzi 

Expected start date: January, May and September of each academic year   

Duration: This is a three-year position

Research context

The PhD opportunity focuses on optimising machine learning for embedded systems. While AI has shown success in solving complex tasks, there is a challenge in training deep learning models directly on embedded devices due to computational complexity exceeding available resources. The project aims to address this challenge by investigating existing learning approaches, developing new machine learning algorithms and proposing optimisation techniques and hardware accelerators for on-device training, on-device continual learning and on-device Bayesian learning. 

Research goal

The goal of this project is to develop novel and efficient learning systems that operate directly on embedded devices, enabling widespread access to the benefits of deep learning in practical and pervasive computing applications. Additionally, the project aims to reduce the carbon footprint associated with deep learning models by adopting low-power solutions. The advancements made through this research will guide the development of next-generation technologies and inspire further research in AI applications on embedded devices. 

Candidate profile

We are seeking highly motivated candidates with a strong background in computer science, machine learning or a related field. The ideal candidate should possess solid programming skills, algorithm design expertise and optimisation techniques. While prior experience or knowledge in embedded systems and deep learning is advantageous, it is not mandatory. Strong analytical skills, problem-solving abilities and excellent communication and collaboration skills are essential for success in this PhD position. 

Further information

This PhD position offers a supportive research environment, access to high-tech facilities and the opportunity to collaborate with leading researchers in the field. The successful candidate will receive a competitive stipend and opportunities for conference participation and publication of research findings. 

Application details

To apply for this position, interested candidates should submit a detailed CV, a cover letter outlining their research interests and motivation for pursuing a PhD in Optimising Machine Learning for Embedded Systems and provide contact information for two academic references. Shortlisted candidates will be invited for an interview to further discuss their research ideas and suitability for the position.   

For further information and application submission, please contact Dr Shidrokh Goudarzi: Shidrokh.Goudarzi@uwl.ac.uk

3. Improving UAV Manoeuvres and Control Using Distributed Sensor Arrays

Primary supervisor: Dr Shidrokh Goudarzi

Expected start date: January, May and September of each academic year   

Duration: This is a three-year position

Research context

In recent years, UAV (Unmanned Aerial Vehicle) systems have undergone significant advancements and it is anticipated that UAV-based services will generate markets valued at over £600 billion by 2050. These services encompass various applications, including the delivery of goods and medical supplies, as well as the inspection and maintenance of energy infrastructure. However, in order for UAV systems to realise their full potential, they must be capable of operating safely in complex environments where challenges such as sensing and predicting external disturbances, obstacle avoidance and manoeuvring in cluttered surroundings arise. Conventional controllers limit the operational capabilities of these aircraft. Therefore, the focus of this PhD research is to overcome this limitation by integrating distributed sensing and nonlinear flight control techniques. 

Research goal

The goal of this PhD research is to evaluate the effectiveness of different flight control strategies that utilise distributed sensing to enable agile UAV manoeuvres. The research will explore three innovative technologies: bio-inspired distributed sensing, machine learning-based flight control and wind tunnel dynamic testing. By applying machine learning techniques, the research aims to develop flight controllers that can effectively utilise the information obtained from distributed sensing arrays. Additionally, algorithms will be designed and simulated to model and analyse the performance of the proposed system. Real aerodynamic conditions will be used to test and evaluate the developed flight controllers. 

Candidate profile

We are seeking a highly motivated and qualified candidate with the following qualifications: 

• A Masters degree (or equivalent) in a relevant field, such as Computer science, Electrical Engineering, Control Systems or a related discipline

• Experience in programming and simulation tools commonly used in aerospace research (eg MATLAB, Simulink, Python)

• Excellent analytical and problem-solving skills, as well as the ability to work independently and as part of a team

• Effective written and verbal communication skills and the ability to present research findings to technical and non-technical audiences

• Familiarity with machine learning techniques and their application in control systems is highly desirable

Further information

The successful candidate will join a dynamic research team with access to high-tech facilities and resources. The research project offers a unique opportunity to work on cutting-edge technologies in UAV control and manoeuvring. The candidate will collaborate with experts in the field and have the chance to present research findings at international conferences and publish in reputable journals. Additionally, there may be opportunities for industrial collaborations and technology transfer. 

Application details

Interested candidates are invited to submit their applications, including the following documents: 

• Curriculum vitae (CV) detailing educational background, research experience and publications (if any)

• A cover letter explaining the candidate's research interests, relevant background and motivation for pursuing a PhD in the proposed research area

• Contact information for at least two academic or professional references who can provide letters of recommendation

• Any additional supporting documents showcasing the candidate's research or technical expertise (optional)

For further information and application submission, please contact Dr Shidrokh Goudarzi: Shidrokh.Goudarzi@uwl.ac.uk

4. Smart City Big Data Processing with AI Algorithms

Primary supervisor: Dr Shidrokh Goudarzi

Expected start date: January, May and September of each academic year   

Duration: This is a three-year position

Research context

As cities worldwide embrace the concept of smart cities, the generation of vast amounts of data presents an unprecedented opportunity to improve urban planning, resource management and overall quality of life. However, effectively processing and extracting meaningful insights from this abundance of smart city data requires advanced AI algorithms. This PhD opportunity focuses on developing innovative approaches that leverage AI algorithms, particularly machine learning and deep learning models, for efficient and effective processing of big data in smart city environments. The aim is to enhance urban planning, resource allocation and sustainability through intelligent systems and decision-support tools. 

Research goal

The objective of this PhD research is to develop novel approaches that harness the power of AI algorithms, specifically machine learning and deep learning models, to process big data in smart city environments. The successful candidate will explore the application of these algorithms in various aspects of data processing, including data collection, integration, analysis and visualisation. Key research areas may include intelligent data analytics, predictive modelling, anomaly detection and optimisation techniques. The research outcomes will contribute to the advancement of smart cities by enabling more informed decision-making and improving resource allocation.

Candidate profile

We are looking for highly motivated candidates with the following qualifications: 

• A Masters degree (or equivalent) in computer science, data science or a related field with a focus on AI algorithms, particularly machine learning and deep learning models

• Solid programming skills and experience in data processing and statistical analysis

• Strong analytical and problem-solving abilities

• Excellent communication and collaboration skills

• Demonstrated research potential through previous projects, publications or relevant work experience

• Familiarity with big data technologies and tools is advantageous

Further information

The selected candidate will join a multidisciplinary research team and have access to real-world smart city datasets and challenges. The research project offers a stimulating environment with high-quality facilities, collaboration opportunities with leading researchers and industry experts and a competitive stipend. Additionally, there will be opportunities for conference participation and publication of research findings, enhancing the candidate's profile and visibility in the field of smart cities and AI.

Application details

To apply for this PhD position, please submit the following documents: 

• A detailed curriculum vitae (CV) that includes your educational background, research experience, publications (if any) and any relevant work experience

• A cover letter outlining your research interests, motivation for pursuing a PhD in Smart City Big Data Processing with AI Algorithms and how your background aligns with the research goals

• Contact information for two academic references who can provide letters of recommendation

• Any additional supporting documents that highlight your research or technical expertise (optional)

For further information and application submission, please contact Dr Shidrokh Goudarzi: Shidrokh.Goudarzi@uwl.ac.uk

Primary supervisor: Dr Gerard Roma

Start date: January, May and September of each academic year

Duration: 3 years

Research context

Audio time scale modification is commonly used to modify the duration of digital audio recordings. Current techniques are generally blind to the content of the recordings being processed and tend to “stretch” audio, which produces artefacts. Practitioners often try different algorithms depending on the audio material. In many cases, the desired effect would be to change the timing of the underlying process (for example, the tempo of a musical piece, or the speed pace), which requires the algorithm to be aware of the content of the signal. Deep learning methodologies are increasingly successful for content-aware signal processing in different media.

Research goal

This project will develop new datasets and novel deep learning architectures for changing the duration of specific types of signals, such as drum recordings or speech, with a focus on the realism of the resulting signal, taking advantage of recent advances in generative deep learning.

Candidate profile

The successful candidate should have a solid background in computer science or a related technical discipline, as well as a deep understanding of audio and music production problems and techniques. In particular, we require:

• Solid knowledge of audio and signal processing
• Good understanding of statistics and machine learning
• Excellent programming skills, particularly in Python
• Experience with established deep learning libraries, such as PyTorch, Tensorflow or Keras

Further information

Questions regarding academic aspects of the project should be directed to Dr Gerard Roma: gerard.roma@uwl.ac.uk

Principle supervisor: Dr Sama Aleshaiker 

Research area: 

Enabling AI and ML Integration for Maximizing the Potential of IoT Technologies 

Pioneering AI and ML Applications for E-Learning 

Personalized Healthcare Solutions with IoT  

Enhancing the Use of IoT Technologies for Smart Environments

For further information, please contact Dr Sama Aleshaiker: sama.aleshaiker@uwl.ac.uk

Name of proposer: Dr Ikram Rehman 

Supervisory team: Dr Ikram Rehman and Professor Massoud Zolgharni  

Start dates: January, May and September of each academic year. 

Duration: 3 years full-time or 5 years part-time. 

Research scope and aim

Today, 3.6 billion people are connected to mobile and the internet worldwide, which is equivalent to 85% of the world's population. The influx of traffic generated by these many connected people will be a setback for smart cities services, as they will often use the same Information and Communications Technology (ICT) resources as those of other popular high bandwidth-demanding applications (eg interactive video streaming, serious gaming, social networking, etc) in a shared environment. Therefore, further innovation in technologies in smart cities is essential to cope with the high levels of data traffic and the increasing demands for spectral efficiency, energy consumption, high mobility, seamless coverage and the varying requirements of Quality of Service (QoS) and Quality of Experience (QoE).  

The emerging technologies in smart cities will enable people with handheld devices to experience high QoS and QoE down at the street level where it really matters. Therefore, utilising emerging technologies (eg 5G networks, Internet of Things, Artificial Intelligence, etc) will greatly impact how future smart cities are being designed both at the technological and infrastructure level. It is also time that such a significant breakthrough in ICT is also reflected in the smart cities context and defined as "The transformation of smart cities and its applications towards user-centric, personalised intelligent systems with high capacity, seamless functionalities with anyhow, anytime and anywhere access."

With a rise in smartphones, wearable devices, Internet of Things (IoT), the surge in the adoption of Artificial Intelligence (AI) and Virtual and Augmented Reality (VR)/(AR) have provided aspiration for people to lead a better Quality of Life (QoL). Some of the sectors that can benefit from technology-enabled smart cities are public areas, transportation systems and home systems, to name a few. 

Through the use of artificial intelligence, availability of data, high bandwidth (eg 5G network), a large number of connected devices and the collaboration of the citizens in a smart city, citizens' lives can be improved by providing them with more independence and safety. Moreover, smart cities also support the concept of sustainable economic growth and the well-being of their citizens. They include, for example, more efficient ways of lighting up buildings, leading towards a greener environment, safer public spaces as well as more interaction for the physically disabled. Their development relies on robust network infrastructure, the internet and their success depends upon the collective intelligent workforce designing initiative and cost-effective solutions.  

The adoption of the smart city is still in its infancy. Besides, user acceptance in terms of accessibility, privacy and security are its pre-requisites.  

I invite potential candidates/researchers to join the Intelligent Sensing and Vision (IntSaV) research group at the University of West London to pursue their doctoral studies with innovative ideas in the following areas:  

• Security, privacy and trust in smart cities 

• Ambient Assisted Living (AAL) 

• Smart Transportation 

• Smart Homes 

• Smart Health 

• Smart Education 

• Smart users experience of smart cities 

• Role of IoT in the smart grid technology for smart cities 

• Cross-layer design for bandwidth-demanding smart applications over 5G networks 

• Artificial Intelligence, machine learning and deep learning utilisation in smart cities 

For a more detailed description of the current PhD projects, please visit the Intelligent Sensing and Vision Research Group website.

Questions regarding academic aspects of the project should be directed to Dr Ikram Rehman: Ikram.Rehman@uwl.ac.uk

Supervisory team: Dr Eugenio Donati and Dr Gerard Roma 

Start date: February 2024 

Duration: 3 years full-time or 5 years part-time 

Research context

Accessibility is one of the most impactful aspects of Automatic Speech Recognition (ASR) as it increases the modalities of interaction with computers and smartphones. This capability however can become hindered in the presence of speaking disabilities.  

An instance of such obstacle is found in users affected by dysarthria. Such condition displays a disorder in the use of speech muscles causing a poor articulation of phonemes and in turn slow and slurred speech. ASR could strongly support accessibility for people affected by dysarthria allowing them to increase their efficiency in communication. Most ASR systems, however, are traditionally trained and tuned with typical speech and tend to have poor performances when being presented with dysarthric speech input.

Research goals

This project aims at enhancing the performances of ASR systems for dysarthric users by developing novel deep learning models as well as expanding the current availability of datasets and assessment methodologies. This is to be integrated with existing ASR systems through Transfer Learning as well as investigating the implementation of tailored Neural Network architectures. 

Candidate profile

The ideal candidate should hold an MSc or equivalent degree in relevant subjects within the area of Computing and Audio Engineering. In particular, the candidate is required to have a solid understanding of Audio Signal Processing, good programming skills and a good understanding of Machine Learning.

Questions regarding academic aspects of the project should be directed to Dr Eugenio Donati: eugenio.donati@uwl.ac.uk  

Primary supervisor: Dr Lavanya Srinivasan

Expected start date: January, May and September of each academic year 

Duration: This is a three-year position

An unexpected incident that forces people to take a seat on the lower level (floor or ground) is referred to as a fall. It consequently results in injuries that frequently have catastrophic consequences. Psychological complaints are also seen as a fall's aftermath. Anxiety, depression, activity restriction and a fear of falling are all possible mental health conditions.

The main physiological problem that older people face is a fear of falling, which limits their day-to-day activities. Because of their dread, older persons restrict their activities, which can impair their mobility and independence by weakening their muscles and causing inadequate gait balance.

Understanding falls can be categorised into two categories for this purpose: fall detection and fall prevention. Fall detection is the process of identifying a fall by tracking a person's movements. Fall detection is the practice of spotting a fall by observing someone moving. Therefore, the research incorporates  machine learning and deep learning for high classification accuracy for fall detection and prevention.

For more information about the project, please contact Dr Lavanya Srinivasan: lavanya.srinivasan@uwl.ac.uk 

Supervisory team: Dr Lavanya Srinivasan

Expected start date: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time 

Research context

Many learning tasks involve working with graph data, which has rich relational information between elements. Graph reasoning models are also needed for an important research topic: learning from images and reasoning on extracted structures such as the scene graphs of images. Graph neural networks, or GNNs, are neural models that use message passing between graph nodes to capture the dependence of graphs. Variants of graph neural networks like graph recurrent networks (GRNs), graph attention networks (GATs) and graph convolutional networks (GCNs) have shown remarkable results on a variety of deep learning tasks in recent years.

The application's graph structure must first be determined. Typically, there are two types of scenarios: non-structural scenarios and structural scenarios. Knowledge graphs are examples of applications where the graph structure is explicitly defined in structural scenarios. Graphs are implicit in non-structural scenarios, construct the task's graph, such as creating a scene graph for an image. The most rapidly evolving area of AI research is image (computer vision), which is typically included in non-structural scenarios.

Zero-shot image classification

In N-shot learning, the training set consists of only N training samples from the same classes, which are used to predict the test data samples in those classes. As a result, few-shot learning limits N to be small, while zero-shot learning demands that N equal 0. For models to generate new predictions for testing data, they must be able to generalize from the small amount of training data. In contrast, graph neural networks can help the image classification system in these difficult situations.

Visual reasoning

Computer vision systems must be able to reason by combining semantic and spatial information. Visual question answering is a task in visual reasoning (VQA). Given the text description of the questions, a model must respond to the questions regarding an image. The spatial relationships between the objects in the image usually hold the key to the solution. Knowledge graphs enable more interpretable reasoning processes and more thorough relation exploration. Object detection, interaction detection and region classification are applications of visual reasoning. 

Semantic segmentation

An essential first step in understanding images is semantic segmentation. A dense classification problem is one in which each pixel in the image has a distinct label. Areas in pictures are frequently not grid-like and require information that is not local, which results in the failure of the classic CNN. It is natural to consider graph-structured data to handle it such as Graph-LSTM.

Prospective PhD candidates may also suggest their own topics.

Eligibility Criteria

Masters in Computer Science / Engineering. If English is not your first language, you will require an IELTS score of at least 6.5 overall, with no element under 6.0. 

Those who are interested in working on the aforementioned projects contact the supervisor for more details. Supervisor Dr. Lavanya Srinivasan: lavanya.srinivasan @uwl.ac.uk

Supported by the Intelligent Sensing and Vision (IntSaV) Research Group, the School of Computing and Engineering offers exciting PhD opportunities for advanced research in all aspects of Artificial Intelligence and Big Data analysis.

The IntSaV group has a strong training focus aimed at developing the next generation leaders in the field of computer science. We have several exciting and future opportunities, covering a diverse range of research topics such as:

• Machine Learning and Deep Learning
• Application of AI in Pharmaceutical Science
• Explainable and Responsible AI
• Computer Vision
• Medical Image and Signal Analysis
• AI-Enabled Wearables for Healthcare Diagnosis
• Human-Centred Artificial Intelligence
• Unmanned Aerial Vehicle (UAV) Manoeuvres and Control
• Smart City Big Data Processing
• 3D Bioprinting and Tissue Engineering

For a more detailed description of the current PhD projects, please visit our Github page.

The PhD students working on multidisciplinary research projects are supported by a sizeable supervisory team with subject specialist knowledge:

• Professor Massoud Zolgharni
• Professor Jonathan Loo
• Dr Neda Azarmehr
• Dr Lavanya Srinivasan
• Dr Ikram Ur Rehman
• Dr Ali Gheitasy
• Dr Eugenio Donati
• Dr Sama Aleshaiker
• Dr Laden Husamaldin
• Dr Nasser Matoorianpour
• Dr Shidrokh Goudarzi
• Dr Nasim Dadashi Serej
• Dr Hanieh Khalili

Start dates: January, May and September of each academic year. 

Duration: 3 years full-time or 5 years part-time. 

Candidates’ profile: Entry requirements for our PhD course: 

• First or Upper Second class (2:1) or equivalent in a relevant field 

• MSc degree with Merit or above or have equivalent postgraduate or research experience

• International applicants only: an IELTS score (International English Language Testing System) of 6.5 or higher (with no element under 6.0) for international applicants. Applicants with a previous degree obtained in the UK are exempt from this requirement

Available PhD projects:

We are always looking for talented PhD candidates to join our group. For more information about available PhD opportunities, please contact the relevant faculty members within the IntSaV research group.

Vice-Chancellor’s PhD Scholarships:

The University of West London is offering a number of opportunities for three year fully funded PhD Scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international students. Learn more

For general enquiries about the PhD in Computer Science, please see our PhD course page

Primary supervisor: Dr Neda Azarmehr 

Expected start date: January, May and September of each academic year    

Duration: This is a three-year position

Salivary gland cancers (SGC) are categorised as head and neck cancers and account for approximately 0.5% of all malignant tumours. They present complex malignancies with more than 35 subtypes and there is a significant overlap in features between 15 benign SGT and 20 malignant SGCs. This complexity presents significant diagnostic and therapeutic challenges.

Radiological investigation such as MRI is a standard of care in the diagnosis and treatment planning of SGTs. MRI scans can help determine the exact location and extent of a tumour (for example, if it is growing into nearby tissues). However, radiological interpretation is subjective and it is not enough to accurately predict the type of behaviour of cancer on radiology alone. Histopathological examination supplements the radiological information through analysis of several pathological features including morphology, grade.

Artificial Intelligence (AI) algorithms are being used to facilitate cancer diagnosis and prediction by improving efficiency and providing quantitative outputs for treatment decisions. However, the application of AI algorithms regarding SGC has been limited. This is essential as SGC are rare (compared to the more common cancers) and an under-investigated research area with poor existing knowledge of the mechanisms of disease progression.

This PhD proposal aim to develop a novel, automated RadioPathomic integrated Artificial Intelligence system to provide identification, subtyping of Salivary Gland Cancers and predict prognosis.

For more information about the project, please contact Dr Neda Azarmehr: neda.azarmehr@uwl.ac.uk

Primary supervisor: Dr Neda Azarmehr

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Research context

This project aims to develop an AI-based tool to automatically detect cancerous cells that have spread around nerves in head and neck cancers, a leading global cancer type with significant annual incidence rates in the UK. The project focuses on perineural invasion (PNI), a key factor in the disease's progression and poor survival rates, which currently relies on slow and inconsistent traditional histological examinations.

By employing advanced AI and deep-learning technologies, this project aims to improve the precision and efficiency of PNI detection, ultimately enhancing prognostic assessments and enabling personalised treatment plans for patients.

Candidate profile

PhD applicants for this project must have previous experience in AI projects, proficiency in Python programming, as well as hands-on experience with TensorFlow or PyTorch.

Further information

For further information and application submissions, please contact Dr Neda Azarmehr: neda.azarmehr@uwl.ac.uk 

Primary supervisor: Dr Neda Azarmehr

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Research context

Lung cancer, the second most common cancer globally, significantly impacts survival due to late-stage diagnoses. Over half of these cases are Non-Small Cell Lung Cancer (NSCLC), which often involves costly and time-consuming molecular testing not widely available, leading to delays in treatment.

This project aims to develop an AI model to predict molecular mutations from whole slide images, aiming to enhance diagnostic accuracy, reduce costs and improve treatment personalisation by integrating clinical and pathological data. Such advancements are not just about enhancing clinical outcomes by enabling earlier and personalised treatments, but are also about easing the financial strain by cutting down the costs associated with conventional molecular testing.

Candidate profile

PhD applicants for this project must have previous experience in AI projects, proficiency in Python programming, as well as hands-on experience with TensorFlow or PyTorch.

Further information

For further information and application submissions, please contact Dr Neda Azarmehr: neda.azarmehr@uwl.ac.uk 

We have a number of exciting opportunities for PhD students in the areas of Artificial Intelligence and machine learning, for the research topics specified above or similar. Candidates can also have their own proposals for a PhD topic.

Prospective students will have a good first degree in Computer Science, Mathematics, Physics or another engineering-related discipline.

Titles of some past/future projects:

• Neural microcircuits of learning and cognitive modelling
• Machine Learning in Spiking Neural Networks implemented
• Machine Learning in Spiking Neural Networks implemented on SpiNNaker platform
• Intelligent Closed-Loop Electronic Systems for Neuromodulation
• Neuromorphic Robotic System
• Trajectory recognition using Spike-Timing-Dependent Plasticity on SpiNNaker and a Dynamic Vision Sensor
• Robotic Sensory and Motor Functions Implemented on Neuromorphic Hardware
• Robogoalie Implemented on Neuromorphic Hardware
• Machine Vision Tasks using an Asynchronous, Event-based Spatiotemporal Vision Sensor
• ML algorithm for cancer diagnostic inference
• Advanced Bio-sensors

Information about PhD opportunities is available by directly contacting Professor Konstantin Nikolic: konstantin.nikolic@uwl.ac.uk

Principal supervisor: Professor Ali Bahadori-Jahromi (Sponsored by The London College, Civil Engineering Division)  

Start date: January, May and September of each academic year

Project summary:

Indoor environment quality plays an important role in people’s lives as they may have to spend 60% of their lifetime in buildings. In past decades, indoor environment quality studies mainly targeted the combination and balance of promoting occupiers’ comfort and reducing energy consumption in buildings.  However, the pandemic has changed the demands or their proprieties of building users as the health and safety against virus spreading come up as the most important requirement in a building. Educational buildings as a type of highly dense indoor environment play an important role to provide from virus spreading. Based on the current medical research, it claims that the COVID-19 and its variants will be with us for a long time. Therefore, it becomes very timely and important to re-exam and improve our building of indoor environments while the impact of such modification will be considered in relation to energy performance of the building for achieving nearly zero energy as well as CO2 and greenhouse gas emissions. 

Project objectives: 

This is a collaborative project between the University of West London and The London College. 

The project will: 

• Investigate environmental performance of the currently available ventilation systems that are used in educational buildings and assess their impacts on thermal performance of the building 
• Design a safe educational building while considering thermal comfort and energy efficiency  
• Establish a set of recommendations for improving the carbon footprints and greenhouse gas emissions during the operational phase while maintaining indoor environment quality

Qualification:

This is an opportunity for an exceptional candidate. Applicants must have obtained, or be about to obtain, at least Merit on a Master of Engineering (MEng) or Master of Science (MSc) in a relevant area including Civil or Mechanical Engineering. Applicants must also have a keen interest in energy and ventilation issues combined with an excellent knowledge of statistical analysis and computational simulation.

Funding:

This project is funded for three years to cover the cost of tuition fees and to provide a stipend of £12,000 per year. 

Further information:

For more details contact Professor Ali Bahadori-Jahromi: ali.jahromi@uwl.ac.uk 

Primary supervisor: Professor Ali Bahadori-Jahromi

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This is a collaborative study between University of West London, The Chartered Institution of Building Services Engineers (CIBSE) and Hilton Worldwide. This research focuses on the study of nearly zero energy buildings (NZEB) standards and examines how they can be achieved when considering the projected changes in our climate. Dr Jahromi and his industrial partners are leading the vibrant research team of three current candidates in this area and past graduates which provides the selected applicant appropriate support and welcoming environment in undertaking the research.

The Directive 2010/21/EU of the European Parliament on the energy performance of the buildings states that by 31 December 2020, all new buildings are nearly zero-energy buildings; and that after 31 December 2018, new buildings occupied and owned by public authorities are nearly zero-energy buildings. The UK Climate projections show that we can expect warmer and wetter winters along with hotter and drier summers. 

Research goal

Research will investigate how the NZEB standards can be achieved for the future climate. It will consider the current practices for designing a NZEB and examine if they are applicable when considering future weather conditions. It will study both a dwelling and a government building and will provide design guidelines.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil or Mechanical Engineering. Potential candidates should have experience in energy simulation software packages. Confidence in computational simulation and CFD packages will be advantageous.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali Bahadori-Jahromi: ali.jahromi@uwl.ac.uk

Primary supervisor: Professor Ali Bahadori-Jahromi

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering.

The prime goal of professionals in the built environment is to develop cost effective sustainable buildings which contribute to the attainment of climate change mitigation goals, facilitate the achievement of indoor thermal comfort and reduction of building energy demand. 

Research goal

This research focuses on the viability of passive solar design strategies of UK for 1950s dwellings and shows that passive solar energy utilisation in building design can contribute to the reduction of dwelling energy consumption and enhancement of indoor thermal comfort. 

Synergetic passive design strategies that seek to optimise solar energy gains through thermal simulation analysis of design criteria of varying future climatic conditions, variable occupant behaviour, building orientation, adequate provision of thermal mass, advance glazing, appropriate ventilation and sufficient level of shading which influence the potential thermal performance is performed. 

The balance energy benefits of reduction of energy consumption through the application of these principles of passive solar design for space heating in winter and the challenge of reducing excessive solar gains in summer will be analysed by using the Computational Fluid Dynamic (CFD) method along with the CIBSE adaptive thermal comfort criteria. 

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil or Mechanical Engineering. Potential candidates should have experience of thermal simulation for existing or new buildings. Confidence in AutoCAD and Revit will be advantageous.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali Bahadori-Jahromi: ali.jahromi@uwl.ac.uk

Primary supervisor: Professor Ali Bahadori-Jahromi

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

This research seeks to address the environmental concerns associated with cement production, known for its significant carbon emissions. By exploring alternative materials as potential full replacements for conventional cement, this study aims to determine the viability and benefits of such substitutions. Initial investigations will focus on the workability, density, mechanical properties, and microstructural analysis of concrete. The findings from this research could pave the way for more sustainable construction methods, potentially transforming the industry by offering both environmental and economic advantages.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Master’s degree (or equivalent) in Civil or Mechanical Engineering. The ideal candidate should have hands-on experience in concrete mix design and laboratory testing. They should possess prior research or work experience specifically in alternative cementitious materials. Their expertise should extend to a variety of testing methods essential for evaluating the mechanical attributes of concrete. Additionally, a deep-rooted interest in sustainable construction and an understanding of the environmental concerns linked to the construction industry are crucial. Familiarity with the current sustainability challenges faced by the construction sector is a must.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at an overall 7 (with 6.5 in all four skills).

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali Bahadori-Jahromi: ali.jahromi@uwl.ac.uk

Primary supervisor: Professor Ali Bahadori-Jahromi

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This is collaborative study between University of West London, The Chartered Institution of Building Services Engineers (CIBSE) and Hilton Worldwide. This research focuses on the study of different façade types and the effect they have on the energy performance of the buildings and what are the ongoing maintenance requirements. Professor Jahromi and his industrial partners are leading the vibrant research team of three current candidates in this area and past graduates which provides the selected applicant appropriate support and welcoming environment in undertaking the research.

The façade is the boundary of the building between the interior spaces and the external area. It has a significant impact on the interior temperature and lighting consumption, therefore it affects the energy performance of the building. Research will also have to take into consideration the climate change and investigate potential overheating issues that the buildings might have to encounter in the future. 

Research goal

The research concentrates on understanding the effect of the façade on the energy performance of the building. Research will investigate different façade types for various types of buildings and for various climates. It will aim to identify retrofitting solutions for existing buildings and to provide guidelines for the characteristics of the façade of new buildings. 

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil or Mechanical Engineering. Potential candidates should have experience in energy simulation software packages. Confidence in computational simulation and CFD packages will be advantageous.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali Bahadori-Jahromi: ali.jahromi@uwl.ac.uk

Primary supervisor: Professor Ali Bahadori-Jahromi

Start dates: January, May and September of each academic year

Research context

The research will build upon a preceding study conducted at London College, UK, which investigated indoor CO2, NO2, PM2.5 and SARS-CoV-2 levels, focusing on their detrimental effects on human health within a higher educational building. The initial study utilised CONTAM software for analysing Indoor Air Quality (IAQ) and building airflow rates and ventilation, exploring various IAQ enhancement strategies. The next phase of the study aims to incorporate Artificial Intelligence (AI) to further optimise ventilation systems, focusing on maintaining desired IAQ levels while minimising energy consumption. 

Research goal

The primary objective of this research is to develop an AI model capable of optimising the operation of ventilation systems. The model will analyse data from various sensors, make predictions about IAQ under different conditions, facilitate real-time monitoring of IAQ and send alerts when the levels of contaminants exceed safe limits. The ultimate goal is to enhance the living and learning environment in educational buildings by adhering to standardised regulations and considering various mitigation strategies like increased ventilation and the use of air cleaners. 

Candidate profile

The ideal candidate should have a strong educational background in any of the following areas: Built Environment, Civil Engineering, Mechanical Engineering or Computing. Proficiency in Python and experience with Machine Learning are essential for the development and implementation of AI models in this project.

Further information

For any academic-related queries regarding the project, please contact Professor Ali Bahadori Jahromi: Ali.Jahromi@uwl.ac.uk

Application process

Interested candidates are encouraged to review the detailed project outline and submit their applications, including a CV and a cover letter detailing their qualifications and research interests, to the provided contact.

Primary supervisor: Professor Ali Bahadori-Jahromi

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This research focuses on compression anchorage lengths in reinforced concrete.

Background to this industry initiated research comes from the fact that it is frequently debated whether the Planned Preventive Maintenance should be conducted on a time-frequency basis (ie every three months change a filter in Fan Coil Unit (FCU)) or should it be driven by the demand (ie have a sensor installed on a filter that would indicate the need of cleaning or replacing). Similar policy would apply to other mechanical and electrical plants, however it is not known yet whether this would be beneficial in the long term from the point of view of guest satisfaction/internal environment and of course cost savings/expenses.
     
This is collaborative study between University of West London, The Chartered Institution of Building Services Engineers (CIBSE) and Hilton Worldwide. Dr Jahromi and his industrial partners are leading the vibrant research team of three current candidates in this area and past graduates which provides the selected applicant appropriate support and welcoming environment in undertaking the research.

Research goal

This study aims to investigate and evaluate the relationship between energy consumption, planned preventive maintenance (PPM) regime, internal environment, cost benefits and guest satisfaction. This research involves energy and internal climate computational modelling as well as in-situ measurements and occupant surveys to understand the full picture.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil or Mechanical Engineering. Potential candidates should have experience of computational simulation. 

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali Bahadori-Jahromi: ali.jahromi@uwl.ac.uk

Primary supervisor: Dr Umair Khalid

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Topic

An investigation on construction techniques, methods and policies to achieve net-zero sustainable goals for building and cities.

Research context

The increasing demand for sustainable urban development has brought the concept of "smart buildings" and "smart cities" to the forefront of discussions around environmental responsibility and the future of urban living. Achieving net-zero emissions in buildings and cities – where energy consumption and emissions are balanced by renewable energy production and carbon offsetting – has become an essential goal for mitigating climate change.

By 2050, over 68% of the global population is expected to live in cities, which are already responsible for more than 70% of global CO2 emissions. This rapid urbanisation puts immense pressure on existing infrastructures and energy systems, exacerbating environmental degradation and resource depletion. The construction industry plays a critical role in this, as traditional building practices consume large amounts of energy and resources, contributing significantly to greenhouse gas emissions. Research into new construction methods and technologies that can reduce the environmental impact of urban development is crucial for achieving sustainability in cities. Smart buildings and cities, powered by renewable energy and optimised through digital technologies, can help mitigate the environmental costs of urbanisation.

Research goal

This research on smart buildings and cities aims to explore innovative construction techniques, technologies, methods, and policies that can drive the transition toward net-zero urban environments.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Computing, Built Environment, Architecture, Civil Engineering or similar.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at an overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Umair Khalid: umair.khalid@uwl.ac.uk 

Primary supervisor: Dr Umair Khalid

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Topic

Research on Artificial Intelligence (AI) and other digital technologies (BIM, IOT, MR) in improving decision-making and project management efficiency.

Research context

The construction industry is undergoing a digital transformation driven by rapid advancements in technologies such as artificial intelligence (AI), building information modelling (BIM), the Internet of Things (IoT), and machine learning (ML). These innovations offer tremendous potential to improve decision-making processes, enhance project management efficiency, reduce costs and mitigate risks. However, the adoption and integration of these technologies within the industry have been slow compared to other sectors like manufacturing and finance.

Research goal

This research seeks to explore how these technologies can be integrated into construction practices, providing a roadmap for future digital innovation in the sector. The findings will not only contribute to the academic understanding of digital transformation but also offer practical insights for industry stakeholders seeking to enhance productivity, efficiency and safety in construction projects.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Built Environment, Architecture, Computing, Civil Engineering or similar.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at an overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Umair Khalid: umair.khalid@uwl.ac.uk 

Primary supervisor: Dr Umair Khalid

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Topic

Investigate the application of Virtual Technology to mitigate H&S risks from construction sites.

Research context

Health and safety risks remain a significant concern in the construction industry, where accidents, injuries and fatalities occur at higher rates compared to many other sectors. Construction sites are inherently hazardous due to the nature of the work, the use of heavy machinery, working at heights and the constantly changing environment. Despite the presence of health and safety regulations and protocols, the industry continues to face challenges in effectively mitigating risks. This research focuses on the application of virtual technologies, such as virtual reality (VR), augmented reality (AR) and building information modelling (BIM), as tools to improve health and safety management on construction sites.

The application of virtual technologies to health and safety management presents a transformative opportunity to mitigate risks on construction sites. By providing immersive training, enabling real-time hazard identification, and facilitating proactive risk management, technologies such as VR, AR, and BIM can significantly reduce the frequency and severity of accidents in the construction industry.

Research goal

The research could be carried out on the following areas;

• Evaluate the effectiveness of virtual reality (VR) in enhancing safety training
• Analyse the role of VR in hazard identification and risk assessment
• Determine the cost-effectiveness of implementing VR for health and safety management
• Develop best methods for VR integration in health and safety management

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Computing, Built Environment, Architecture, Civil Engineering or similar. Working experience in virtual technologies and programming would be beneficial.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at an overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Umair Khalid: umair.khalid@uwl.ac.uk 

Primary supervisor: Dr Umair Khalid

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Topic

Implementing lean principles to minimise waste and maximise efficiency in construction projects.

Research context

The construction industry has long faced challenges related to inefficiency, cost overruns, project delays and material waste. Traditional construction practices often result in significant waste – both in terms of physical materials and inefficient processes. In response to these issues, lean management, originally developed in manufacturing, has gained traction in the construction sector as a framework for optimising processes, reducing waste and improving efficiency. Lean principles, which emphasise value creation for the customer, waste elimination and continuous improvement, offer a pathway to address the chronic inefficiencies in construction projects. This research focuses on the application of lean management principles in construction to enhance project performance, sustainability and productivity.

Research goal

This research will investigate the applicability of lean principles in construction, exploring how they can be tailored to fit the industry's unique needs and examining the benefits of lean practices for environmental sustainability, quality control, collaboration and workforce productivity. The findings from this research will contribute to a deeper understanding of how lean management can revolutionise construction practices, ultimately leading to more efficient, sustainable and cost-effective projects.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Computing, Built Environment, Architecture, Civil Engineering or similar.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at an overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Umair Khalid: umair.khalid@uwl.ac.uk 

Primary supervisor: Dr Umair Khalid

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Topic

The research on sustainable building materials/technologies and their impact on construction efficiency and sustainability.

Research context

The global construction industry is a significant contributor to environmental degradation, resource depletion and energy consumption, accounting for a substantial portion of carbon emissions and waste production. As urbanisation increases and demand for infrastructure grows, the need for more sustainable practices in construction has become urgent. Traditional construction practices heavily rely on materials like concrete, steel and timber, which are energy-intensive and lead to large carbon footprints. According to studies, the production of cement alone contributes to approximately 8% of global carbon dioxide emissions. The depletion of natural resources for construction, alongside the environmental pollution associated with material production, necessitates the exploration of alternative, eco-friendly building materials and methods. 

Research goal

The research on sustainable building materials and technologies addresses urgent global challenges such as climate change, resource scarcity and environmental degradation. By exploring innovative materials and construction techniques, this research aims to enhance the efficiency of construction practices while promoting long-term sustainability. The outcomes will not only benefit the construction industry by reducing costs and waste but will also contribute to broader efforts to mitigate the environmental impact of human activity.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Built Environment, Architecture, Civil Engineering or similar.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at an overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Umair Khalid: umair.khalid@uwl.ac.uk 

Primary supervisor: Professor Charlie Fu

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Research context

This project aims to apply BIM technology in the teaching of Built Environment courses including:

• Architectural Technology
• Building Surveying
• Quantity Surveying
• Construction Project Management
• Construction Management
• Property Management
• Facilities Management

Within these Built Environment courses, there are many knowledge and technologies can be presented based on BIM models and other extended media.

This can not only give students more 3D interactive and explicit presentation of the information about building structures and technologies in teaching these courses, but can demonstrate students how BIM models and technologies can enhance information integration in building projects.

This project focuses on the principles and techniques to convert and integrate the information of the knowledge and technologies into a structure in which the information can be presented into BIM models and also be extracted and organised into various course modules based on the learning outcomes of these modules. And further it enables automatically to generate assessments for the modules as well. This research will also be proposed into an AI based teaching platform.

This project currently is sponsored by the Vice-Chancellor SEED funding. 

Research goal

This project aims to develop an implementation model of a BIM-based platform for HE Built Environment courses to explicit major functionality and data structure of the platform. It also expects to develop a prototype to demonstrate the major functions of the system and to test and analyses in practice.  

Candidate profile

The applicant should have strong built environment education background, and have good understanding of building technologies, building construction processes, BIM applications (such as AutoCAD Revit, ArchiCAD, etc) and relevant BIM implementation knowledge (such as PAS 1192, IFC, etc). The applicant with programming skills will be desirable but not essential. 

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Charlie Fu: Charlie.Fu@uwl.ac.uk

Primary supervisor: Professor Charlie Fu

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research. This PhD position is based in the School of Computing and Engineering. This research focuses on the innovative application and implementation of the advanced IT technology, such as geospatial analysis technology and virtual reality technology in sustainable urban planning and management. 

Geospatial analysis has been becoming a very popular and feasible method and technology to carry out the quantitative analysis of urban environments, which is based on geo-referenced statistics or “big data” from various sources of governmental, non-governmental and business organisations. This will promote the accuracy and quality of urban planning and management.

Virtual reality technology is an advanced computer visualisation technology which could promote the analysis and presentation of urban environment. This can enhance the public’s and planning decision makers’ understanding of both predictable and non-predictable phenomenon of urban environments via interactive visual simulation.  

Research goal

The research aims to implement such technology in the research and practice of urban planning and management, and develop the innovative implementation principles and technology to the knowledge based of urban planning and management via a number of pilot or practical case studies. 

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Computing, Built Environment, Architecture, Civil Engineering or similar. Ideally applicants should have experience in AutoCAD Revit, 3D Max, and/or VRML.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Charlie Fu: Charlie.Fu@uwl.ac.uk

Primary supervisor: Professor Charlie Fu

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research. This PhD position is based in the School of Computing and Engineering. This research focuses on the innovative application and implementation of Building Information Modelling (BIM) technology in building design and management.

BIM technology has been making the revolutionary innovation to the construction industry in last a few years. This research will target the innovative and creative implementation of BIM technology to enable the automation of the analysis and assessment for the sustainable architectural design and the effective management of buildings.

Research goal

The research goal is to develop the principles and a framework of integrating BIM modules and building sustainability assessment. This aims at the automation of design assessment and the promotion of sustainable design.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Built Environment, Architecture, Civil Engineering, Building Service Engineering or similar. Ideally they should have experience in AutoCAD Revit, BREEAM and/or SAP.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Charlie Fu: Charlie.Fu@uwl.ac.uk

Primary supervisor: Professor Charlie Fu

Start dates: January, May and September of each academic year

Duration: 3 years full-time or 5 years part-time

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research. This PhD position is based in the School of Computing and Engineering. This research focuses on the study of sustainable urbanisation in developing countries.

Many developing countries have had fast economic development in the last two decades associated with the development of globalisation. This has led to massive migration from rural to urban areas. The massive migration also brought in the rapid urbanisation in these countries. Meanwhile the shortage of essential urban infrastructure and resources and social injustice cause various environmental, social and economic problems in urban areas. This research will focus on the possibility and potential of easing these problems by innovative planning and land use methods and techniques.

Research goal

The main goal of this research is to identify the typical and special environmental social and economic issues in the urbanisation process of the studied countries, develop and test the innovative land-use planning and controlling methods and techniques to target and ease such problems. Case studies based on a review of the wide literature in such areas will be carried out in the selected countries and the experience of this study will form a unique contribution to the knowledge base of urbanisation in developing countries.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Town Planning, Built Environment, Architecture, Civil Engineering, Social Studies or similar. Ideally applicants already have a good understanding of qualitative and quantitative research methods.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Charlie Fu: Charlie.Fu@uwl.ac.uk

BIM technology has been widely implemented in building projects in recent years. However, most of such implementation only take place from the outline design stage of a building project, which refers to current version of the RIBA Plan of Work.

This project aims to explore the possibility to implement BIM in the Design Briefing stage. It has been divided into two steps research. The first step is the feasibility of BIM implementation in design briefing via a serials interview and analysis. In which, it focuses on the what is the potential technical and practical opportunities, and potential barriers for architects or project teams to use BIM technologies to capture clients’ requirements and to consult with clients about their desired opinions on initial design.

The study also looks at the possible influence to current building project processes, the possible functions and data/information formats to the potential BIM-based design briefing tools.

The second stage will focus on a prototype development based on AutoCAD Revit and Dynamo to demonstrate the possible functions and data formats to support current design briefing process.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Built Environment, Architecture, Civil Engineering, Building Service Engineering or similar. Ideally they should have experience in AutoCAD Revit, BREEAM and/or SAP.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Charlie Fu: Charlie.Fu@uwl.ac.uk

Primary supervisor: Dr Waheed Oseni 

Start dates:  January, May and September of each academic year 

Duration: 3 years full-time or 5 years part-time 

Research context 

Research at the University of West London lives in an ecosystem of interdisciplinary research. This PhD position is based in the School of Computing and Engineering. This research focuses on developing a framework for performance measurement in facilities management for developing economies. 

The nature of performance measurement has transformed over the past few years. In Facilities management, the trend to benchmark against business modes has led to inactivity and a reduction in the implementation of innovative practices. 

Research goal

The research goal is to develop standards and performance benchmarks to be incorporated into a performance measurement framework for continuous improvement in facilities management. 

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Built Environment, Architecture, Civil Engineering, Building Service Engineering or similar.  

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Waheed Oseni: waheed.oseni@uwl.ac.uk 

Primary supervisor: Professor Kourosh Behzadian

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

Urban catchments and their drainage systems face increasing vulnerability during extreme rainfall events. This heightened vulnerability is primarily driven by urbanisation, climate change and population growth. The consequences of such failures can be severe, affecting environmental systems, causing economic damage to infrastructure and properties and leading to social and health-related losses. Therefore, effective flood control management of water infrastructure is of paramount importance for water companies, environmental agencies and local councils.

Artificial intelligence (AI)-based flood early warning systems, when integrated with decision support systems, can significantly enhance the accuracy of proactive flood control management and reduce adverse losses. However, the development of these systems is a complex task that requires attention to various aspects. These include the applying of multiple real-time input resources such as satellite data or weather characteristics, the testing of new and innovative AI modelling techniques, the establishment of a real-time platform and the implementation of more realistic performance assessments.

Research goal

• Advancing current early warning systems and addressing to current gaps 

•  Develop a decision support system for real time control, intelligent monitoring and prediction of water flow in the chambers

• Providing accurate and fast-paced and reliable platform for flood early warning systems

The proposed methodology is demonstrated, tested and verified through a real case study of urban or non-urban water systems.

Candidate profile

Bachelors degree (or equivalent) with First or Upper Second class (2:1) and Master’s degree (or equivalent) with Merit or above in Civil Engineering, Water Engineering, Environmental Engineering, Natural Resources or other similar disciplines.  

International applicants only: an IELTS score (International English Language Testing System) of 6.5 or higher (with no element under 6.0) for international applicants. Applicants with a previous degree obtained in the UK are exempt from this requirement. 

Candidates should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the lab experimentation with real data and conclude with the validation of a proposed solution through a real-life case study. 

Basic/background knowledge in engineering/relevant science and/or previous experience in the following areas, though not mandatory, will be considered very favourably: programming languages (eg MATLAB, Python, VBA or MS Visual Studio C#).

Vice-Chancellor’s PhD Scholarships

The University of West London is offering a number of opportunities for three year fully funded PhD Scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants. Learn more 

Further information

Questions regarding academic aspects of the project should be directed to Professor Kourosh Behzadian: kourosh.behzadian@uwl.ac.uk

Primary supervisor: Professor Kourosh Behzadian

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

The increasing demands for supplying water, energy, collecting surface runoff and wastewater and managing solid waste in urban areas have put unprecedented pressure on urban infrastructure due to several factors, including population growth, urbanisation, fluctuations in water and energy tariffs and aging infrastructure.

As the devastating impacts of climate change become increasingly apparent, it is imperative to adopt a net-zero approach while keeping sustainable development to effectively address our most pressing environmental challenges.

Consequently, there is a crucial need for reliable, resilient and precise sustainable solutions, such as decentralised and microgrid systems such as rainwater harvesting schemes, greywater recycling systems, renewable solar PV, micro-AD and water/energy-efficient micro-components. Furthermore, long-term planning for maintenance and rehabilitation strategies in urban infrastructure should closely align with these objectives.

With aiding of smart and data mining technologies such as artificial intelligence, this study tries to provide a thorough analysis to understand how a comprehensive performance assessment, incorporating both traditional and innovative criteria, impacts urban infrastructure.  

In addition, all this requires comprehensive solutions that facilitate more efficient use of resources and promote resource conservation and maximise recycling/reusing resources and implement carbon reduction strategies across various environmental domains in urban infrastructure including the water industry, waste management systems, air quality control systems, soil management schemes, civil and construction industries and more.

Through intelligent monitoring, optimisation and the careful balance of consumption and generation, these systems have the capacity to significantly reduce the carbon footprint of urban areas and industrial operations. Therefore, they assume a vital role in mitigating environmental issues, combatting climate change, enhancing air quality and forging a path towards a sustainable future. However, the wide application of these smart technologies is still in its early stages and requires further implementation across various environmental issues and systems. 

Research goal

• Advancing current early warning systems and addressing to current gaps 

• Developing a decision support system for real time control, intelligent monitoring and prediction of water flow in the chambers

• Providing accurate and fast-paced and reliable platform for flood early warning systems 

The proposed methodology is demonstrated, tested and verified through a real case study of urban or non-urban water systems.

Candidate profile

Bachelors degree (or equivalent) with First or Upper Second class (2:1) and Master’s degree (or equivalent) with Merit or above in Civil Engineering, Water Engineering, Environmental Engineering, Natural Resources or other similar disciplines.  

International applicants only: an IELTS score (International English Language Testing System) of 6.5 or higher (with no element under 6.0) for international applicants. Applicants with a previous degree obtained in the UK are exempt from this requirement. 

Candidates should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the lab experimentation with real data and conclude with the validation of a proposed solution through a real-life case study. 

Basic/background knowledge in engineering/relevant science and/or previous experience in the following areas, though not mandatory, will be considered very favourably: programming languages (eg MATLAB, Python, VBA or MS Visual Studio C#).

Vice-Chancellor’s PhD Scholarships

The University of West London is offering a number of opportunities for three year fully funded PhD Scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants. Learn more 

Further information

Questions regarding academic aspects of the project should be directed to Professor Kourosh Behzadian: kourosh.behzadian@uwl.ac.uk

Primary supervisor: Professor Kourosh Behzadian

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

The application of cutting-edge digital visualisation technologies, including virtual reality (VR), augmented reality (AR) and digital twins (DT), has the potential to revolutionise our understanding and management of complex environmental systems. Environmental challenges such as flooding, waste management and air pollution require innovative solutions that can enhance decision-making, improve public awareness and optimise resource allocation.

VR immerses users in a simulated environment, AR overlays digital information onto the real world, and DT creates digital replicas of physical systems. These technologies offer new pathways for visualising, monitoring and interacting with environmental data. They aim to advance stakeholder understanding of how cutting-edge digital visualisation technologies can be implemented to address environmental challenges, contributing to more informed decision-making, increased stakeholder engagement and improved environmental sustainability and resilience.

These technologies are still at the earlier stages and their applicability still is a new era to be explored. This research aims to integrate real-time flood forecasting into digital twins to provide effective and efficient early action for stakeholders and communities.

Research goal

• Investigating the current state-of-the-art with a focus on their capabilities and limitations in the context of environmental systems 

• Developing porotype platform for addressing specific environmental challenges, such as flooding, waste management and air pollution. This may include the development of immersive simulations, real-time monitoring interfaces and interactive dashboards

• Evaluating the effectiveness and usability of these technologies in improving decision-making, public engagement and environmental management in the selected domains

The suggested platform is demonstrated, tested and verified through a real but prototype case study.

Candidate profile

Bachelors degree (or equivalent) with First or Upper Second class (2:1) and Master’s degree (or equivalent) with Merit or above in Civil Engineering, Water Engineering, Environmental Engineering, Natural Resources or other similar disciplines.  

International applicants only: an IELTS score (International English Language Testing System) of 6.5 or higher (with no element under 6.0) for international applicants. Applicants with a previous degree obtained in the UK are exempt from this requirement. 

Candidates should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the lab experimentation with real data and conclude with the validation of a proposed solution through a real-life case study. 

Basic/background knowledge in engineering/relevant science and/or previous experience in the following areas, though not mandatory, will be considered very favourably: programming languages (eg MATLAB, Python, VBA or MS Visual Studio C#).

Vice-Chancellor’s PhD Scholarships

The University of West London is offering a number of opportunities for three year fully funded PhD Scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants. Learn more 

Further information

Questions regarding academic aspects of the project should be directed to Professor Kourosh Behzadian: kourosh.behzadian@uwl.ac.uk

Primary supervisor: Professor Kourosh Behzadian

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research. This PhD position is based in the School of Computing and Engineering.

Climate change can be categorised as one of the main drivers of change along with urbanisation and demographics. However, over 90 per cent of climate change impacts will be reflected through water, which emphasises the real need to plan and manage water resources. Acknowledging the high impacts of climate change on water, there is a fact that rapid urbanisation has reached to the point that the majority of the world’s population live in cities. All this makes the urban water systems as one of the most vulnerable components against climate change. Therefore, future planning of urban water systems needs to incorporate climate change adaptation strategies. The adaptation strategies also need to consider other urban system flows such as energy and carbon and pollution. This assessment can be conducted through a metabolism based modelling approach which will be the central to this PhD study.

Research goal

This PhD programme aims to develop potential intervention strategies in urban water systems which are based on climate change adaptation using urban metabolism based approach. The intervention strategies analysed here will be evaluated based on this fact that how they can increase the resilience of urban water systems again different levels of climate change (eg during flood, drought water stress and shock change events). The urban metabolism concept is extensively utilised here which directly deals with the quantification of the overall fluxes of energy, water, materials, nutrients and wastes into and out of an urban region. By utilising urban metabolism modelling, the system bottlenecks and hotspots are identified and thus a more objective intervention strategies can be defined to effectively improve the system performance under extreme events of the plausible climate changes.  

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and/or a Masters degree (or equivalent) in Civil Engineering, Water Engineering, Environmental Engineering, Natural Resources or other similar disciplines. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data and conclude with the validation of a proposed solution through a real-life case study.

Besides basic knowledge in water systems, background knowledge and/or previous experience in the following areas, though not mandatory, will be considered very favourably: programming languages (eg MATLAB, VBA or MS Visual Studio C#), water supply and urban drainage systems.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Kourosh Behzadian: kourosh.behzadian@uwl.ac.uk

Primary supervisor: Professor Kourosh Behzadian

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

Waste management presents a significant modern-day challenge, with rapid populations and modern lifestyle generating ever-increasing amounts of waste. Traditional waste collection and management methods often struggle to keep pace, leading to issues such as deliver failure, environmental pollution and inefficiencies in resource mobilisation.

In this context, the integration of artificial intelligence (AI)-based solutions into waste collection and management processes is of paramount importance. AI can revolutionise the industry by optimising waste collection routes, predicting collection needs and identifying recycling opportunities, as well as optimal design and real-time operation of solutions. This not only enhances the efficiency of waste management but also reduces environmental impacts, conserves resources and ultimately contributes to more sustainable and cleaner urban environments. While application of AI modelling is tested for accuracy improvement, many other aspects, such as dynamic and optimal patterns for real-time management of these systems, still require more exploration.

Research goal

• Optimal design of waste management systems using AI-based modelling 

• Proposing real-time monitoring platform for input and output management of waste management systems 

• Optimal real-time operation of waste management systems using AI-based modelling 

The developed systems are demonstrated, tested and verified through a real-life problem.

Candidate profile

Bachelors degree (or equivalent) with First or Upper Second class (2:1) and Master’s degree (or equivalent) with Merit or above in Civil Engineering, Water Engineering, Environmental Engineering, Natural Resources or other similar disciplines.  

International applicants only: an IELTS score (International English Language Testing System) of 6.5 or higher (with no element under 6.0) for international applicants. Applicants with a previous degree obtained in the UK are exempt from this requirement. 

Candidates should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the lab experimentation with real data and conclude with the validation of a proposed solution through a real-life case study. 

Basic/background knowledge in engineering/relevant science and/or previous experience in the following areas, though not mandatory, will be considered very favourably: programming languages (eg MATLAB, Python, VBA or MS Visual Studio C#).

Vice-Chancellor’s PhD Scholarships

The University of West London is offering a number of opportunities for three year fully funded PhD Scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants. Learn more 

Further information

Questions regarding academic aspects of the project should be directed to Professor Kourosh Behzadian: kourosh.behzadian@uwl.ac.uk

Primary supervisor: Professor Fabio Tosti

Start dates: January, May and September of each academic year

Duration: This is a three-year position

Research context

Highways and pavements suffer damage because of decay or weakness in their structural components. An effective assessment of surface damage and the structural properties of pavement layers can identify the causes and locate the depth of the damage.

However, conventional destructive tests made on-site and in the laboratory are intrusive, time-demanding and costly. To this effect, non-destructive testing (NDT) methods have become popular, and their use is taking over in routine monitoring operations. Amongst others, falling weight deflectometer (FWD) techniques are mostly used in combination with ground-penetrating radar (GPR) to assess the stiffness and thickness of pavement layers.

Parallel to this, interferometric approaches based on the signal feature change detection at different times in both ground-based and satellite conditions have proven effective in assessing transport infrastructure conditions. The rapidity of data collection in ground-based configuration and the coverage of every satellite image collected by Synthetic Aperture Radar (SAR) sensors, allows the evaluation of large infrastructure in both detailed and at the network level in a single data processing flow. Therefore, the outcome of the SAR techniques lends itself to be highly integrated with the existing NDT methods operating at the local level of the infrastructure.

Research goal

The aim of this project is to develop a novel monitoring and assessment methodology for inclusion in new-generation transport infrastructure smart management systems. The integrated approach will rely on condition assessment-based information collected using remote sensing (ground-based and satellite) and GPR techniques and it will serve to prioritise interventions on highway infrastructure assets.

To achieve this aim, analyses of multi-temporal satellite SAR acquisitions will be carried out first to identify areas of concern at the network level in terms of differential settlements in roads and excessive deformation rate of the pavement surface, amongst others. This information will be instrumental to the next stage of the approach, where ground-based SAR and GPR techniques will be used locally to detect source and scale of defects and deformations.

Tests will be carried out in a laboratory environment, on a test-site scale and on the full scale of inspection.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil Engineering, Transport Infrastructure Engineering, Pavement Engineering, Electrical and Electronic Engineering, Aerospace Engineering or other similar disciplines. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of new algorithms and methodologies and the experimentation with real data and conclude with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti: Fabio.Tosti@uwl.ac.uk

Primary supervisor: Professor Fabio Tosti

Start dates: January, May and September of each academic year

Duration: This is a three-year position

Research context

Airfield infrastructures are major investments requiring viable and strategic maintenance and management. In recent years, the application of non-destructive testing (NDT) techniques such as 3D laser scanners, ultrasound techniques, fibre optic sensors and accelerometers, as well as ground-penetrating radar (GPR) technologies, have been developed for use in this area of endeavour. Collected information are incorporated into infrastructure management systems in compliance with budget constraints, service efficiency levels and safety of the operations. However, this information requires routine data collection to build up comprehensive data inventories. These are collected locally with a relatively high time frequency and space density and cause partial or full-service interruption.

Due to this background, a synergetic use of satellite remote sensing and ground-based technologies, such as the GPR technique, can potentially allow for a more effective data collection through the provision of an integrated routine monitoring approach. This can in fact rely on the use of multi-temporal, multi-source and multi-scale information for application at the local level and the full-scale level of investigation. A more comprehensive assessment of the structural integrity of runways and taxiways can be therefore possible.

Research goal

The aim of this project is to develop a novel pavement management approach for airfields based on a systematic and integrated use of remote sensing satellite and GPR data.

To achieve this aim, satellite imaging (medium and high resolution) and GPR techniques will be used to monitor the integrity of airfield runway and taxiway foundations. Analyses of multi-temporal Synthetic Aperture Radar (SAR) acquisitions will be carried out initially to identify areas of concern in terms of millimetre-scale settlements and their deformation velocity evolution in time, amongst others. GPR techniques will be used locally to detect source and scale of defects and deformations.

Tests will be carried out in a laboratory environment, on a test-site scale and on the full scale of inspection.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil Engineering, Transport Infrastructure Engineering, Pavement Engineering, Electrical and Electronic Engineering, Aerospace Engineering or other similar disciplines. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of new algorithms and methodologies and the experimentation with real data and conclude with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti: Fabio.Tosti@uwl.ac.uk

Primary supervisor: Professor Fabio Tosti

Start dates: January, May and September of each academic year

Duration: This is a three-year position

Research context

Decay in structural components of pavements (highways and airfields) is an element of major concern in the management of transport infrastructures. An effective assessment of the strength and deformation properties of pavement layers can identify scale and depth of damage.

However, the prompt detection of early decay and loss of bearing capacity represent real challenges for asset owners. Conventional destructive tests are time-demanding, and they can provide information at the location point of measurements only. Conversely, non-destructive testing (NDT) methods are gaining momentum in pavement infrastructure monitoring. Above all, falling weight deflectometer (FWD) techniques are mostly used in combination with ground-penetrating radar (GPR) to assess the stiffness and thickness of pavement layers.

Measurements must be collected individually through these techniques, and a back-calculation process is required to estimate the stiffness of the pavement layers. This process might be time-demanding and subject to data processing and synchronisation errors. A clear gap in knowledge has been therefore identified in terms of the need for more time-efficient and continuous ground-based inspection methods based on:

• The integration between electromagnetic (eg GPR) and deflection-based techniques (eg FWD, curviameter)
• The use of machine learning (ML) techniques in the analysis process and the implementation of new algorithms

Research goal

This research project will investigate into the mechanical properties and stiffness of construction materials and pavement layers (highways and airfields) using GPR and complementary non-destructive testing methods across the entire range of investigation scales. New ML-based algorithms and methodologies for a more effective data integration from the above testing equipment will be provided.

Tests will be carried out in a laboratory environment, on a test-site scale and on the full scale of inspection.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil Engineering, Transport Infrastructure Engineering, Pavement Engineering, Electrical and Electronic Engineering, Computer Science or other similar disciplines. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of new algorithms and methodologies and the experimentation with real data and conclude with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti: Fabio.Tosti@uwl.ac.uk

Primary supervisors: Dr Mosleh Tohidi and Professor Ali B-Jahromi

Background

The rules to prevent progressive collapse emerged in the codes following the partial failure of the Ronan Point apartment in 1968. The British Regulation (Fifth Amendment) 1970 developed the provisions for an alternate load path, a key element and a tie force method. Following the UK standards, most national codes have adopted these three rules to prevent progressive collapse, eg the American Department of Defence (DoD 2005), the Eurocodes (EN 1992 1- 1:2004), American Society of Civil Engineering (ASCE 7-05) and the General Service Administration (GSA:2003).

The tensile tie force (TF) method is one of the main design approaches for preventing progressive collapse, whereby an indeterminate structure is analysed statically by assuming a failure mode for a locally simplified determinate structure. Current design practice (TF Method) in the UK, EU and USA is mostly based on the descriptive method to specify the tie design, to address the progressive collapse in RC construction.

It is generally agreed that ductility and continuity are the two most important factors to prevent progressive collapse, while the TF method, more or less, is based on strength rather than ductility.

These prescriptive tie requirements may have proven adequate in engineering practice, but are not fully scientifically justified. Therefore, substantial efforts are still needed to improve understanding at a fundamental level of how the mechanism of post-collapse resistance is developed through these tie provisions. This need has also been supported by a number of researchers in the last decade.

Aim

In this study, to overcome the inadequacy of the current TF method in both national and international codes of practices, an improved Tie-Force method to analyse RC structures following one/two column removal is proposed using the Alternate Load Path method and non-linear FE analyses.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisor: Professor Ali Bahadori-Jahromi

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This research focuses on compression anchorage lengths in reinforced concrete.

IN EN1992-1-1, Eurocode 2, factor α2 = 1.0 for bars in compression anchorage is too onerous, especially for starter bars in large bases. There is very little research in this area. Evidence from University of Durham suggests a factor of 0.7 (as for tension anchorage) might be appropriate but this needs verification. Further work is required, especially on large bars and on bends in foundations. There should be benefits from end bearing, bends and confinement in large bases – but none of these is currently allowed. This project would investigate compression anchorage in order to suggest design guidelines.

Research goal

The research concentrates on understanding bond models and compression transfer. This means testing the anchorage of bars in compression to look at the effect of end bearing, end and edge distances, transverse reinforcement, perhaps transverse flexural compression, welded transverse steel. 

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil or Mechanical Engineering. Potential candidates should have experience of finite element (FE) simulation. Confidence in FE packages like ANSYS and LUSAS will be advantageous.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisor: Dr Spyridon Paschalis

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

During the last century, our planet faces great challenges such as global warming, the damage to the ozone layer and the reduction of the supplies of freshwater. It is crucial to focus the research on sustainable development in order to minimise the consumption of natural resources, to minimise the negative environmental impacts and, at the same time, to maintain human satisfaction. The reduction of the environmental impact of infrastructure materials is also of high importance to the UK’s target to become carbon neutral by 2050.

Concrete is the most widely used construction material in the world with some great advantages. However, it is estimated that cement is responsible for almost 5% of the total emissions of CO2.

The present research focuses on the development of sustainable concrete using recycled materials. Also, the present research focus on applications of sustainable concrete for structural purposes.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and/or a Masters degree (or equivalent) in Civil Engineering and/or Structural Engineering. Confidence in Finite Element packages will be advantageous. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives.

Applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Spyridon Paschalis: Spyros.Paschalis@uwl.ac.uk

Primary supervisor: Professor Ali Bahadori-Jahromi

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering.

The prime goal of civil engineers is to develop safe and cost-effective buildings which contribute to protect human lives from natural disasters such as earthquakes. Traditionally, engineers have designed structures to resist earthquake forces so that the strength of structural members are equal to or greater than the seismic demand placed on them. During the 1980s and 1990s, new approaches to seismic design emerged which involved modifying structural response to reduce earthquake induced loads to more tolerable levels (Access Science, 2007). These methods included moment frame, braced frame, shear walls, base isolation (Engineering Structures, 2006), supplemental damping (Engineering Structures, 2005) and active control (Engineering Structures, 2004).

However all these techniques are expensive and the average family cannot afford to built a proper earthquake resistance house. Gurkalo, F. and Poutos, K. (2014), in their research proved that an interlocking panels mechanism improves the response of water towers when subjected to dynamic loading. Therefore, it is expected that the use of such panels could greatly improve the performance of structures under dynamic loading and improve earthquake resistance of tall buildings. 

Research goal

In this research it is proposed to undertake a detailed analytical research study on the dynamic response of different structural systems made with interlocking panels. The proposed study would include both desktop analysis using Finite Element Analysis Software and experimental study using the University’s Facilities. It is anticipated that successful completion of the research study will result in a significant contribution to earthquake and structural engineering science.

The overall aim of the proposed research is to develop a numerical model to simulate the structural behaviour of a building composed of interlocking panels under earthquake loading.

The following research questions/aspects will be addressed through this research study:

• The way in which static and dynamic loads are transferred through a system of interlocking structural panels
• The way in which the complete structure made with interlocking panels responds to dynamic loading – comparison between theory and practice
• The comparative performance, in terms of dynamic response, between a structures composed of interlocking panels and conventional structural systems
• The potential for optimisation of the joining system

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters Degree (or equivalent) in Civil Engineering. Potential candidates should have experience on earthquake design according to EC8. Confidence in the use of any FEA software will be advantageous.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisor: Professor Ibrahim Shaaban

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD

Research context

Geopolymer concrete aims to reduce the CO2 emission by minimising or avoiding cement in concrete. This necessitates adding cement replacement materials such as Silica Fume or GGBFS. However, the durability of geopolymer concrete is questionable, especially for long term effect.

The research aims to test the properties of geopolymer concrete in fresh state and after hardening for short periods and long periods up to 18 months in normal and aggressive environments. Mechanical properties such as compressive, tensile and flexural strengths will be assessed. Microstructure studies will be carried out to explain the mechanical properties. In addition, durability testing such as chloride diffusion and freeze/thaw will be carried out. Moreover, permeation tests will be used to predict the durability. Statistical analysis will be used to verify the output results.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree in Civil Engineering or Structural Engineering. Familiarity with numerical modelling and experimental testing are preferable. The candidate should be able to work in a team and to be committed to work hard to achieve research excellence.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ibrahim Shaaban: ibrahim.shaaban@uwl.ac.uk

Primary supervisor: Professor Ali Bahadori-Jahromi

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This research focuses on lap lengths in reinforced concrete. 

Over the past few decades, design rules have consistently increased lengths required for laps between reinforcing bars in reinforced concrete slabs, beams and other elements to the extent that the buildability and competitiveness of reinforced concrete structures is being seriously compromised. Current rules for tension laps are based on bond tests which use unrealistic constant moment, zero shear specimens. Yet there is evidence to suggest that shear greatly increases the ability of tensile forces to be transferred. This project would investigate this phenomenon in order to suggest design guidelines.

Research goal

This research concentrates on understanding bond models (Tepfers) and then on how shear might affect bond and tension transfer. This means combination of experimental work and FE modelling of the lap mechanism. 

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil or Mechanical Engineering. Potential candidates should have experience of finite element (FE) simulation. Confidence in FE packages like ANSYS and LUSAS will be advantageous.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali Bahadori-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisors: Dr Mosleh Tohidi and Professor Ali B-Jahromi

Background

As the construction industry provides infrastructure and buildings needed for the society and economy, it is responsible for a considerable amount of CO2 emissions resulting from the production of cement, and other greenhouse gases added to the atmosphere due to material production process, construction process, renovation and demolition waste. This situation must be changed before the exhaustible natural resources of the planet run out. Enhancing construction practices to minimise these harmful environmental effects has also drawn the interest of building experts around the world. Although various fields in the construction industry can be taken into account, to reduce concrete in RC structures, sustainability in the design of different structural elements in RC structures needs to be considered as a priority.

Aim

The aim of this study is to explore the potential of minimising embodied carbon in RC structures with flat slabs considering minimum slab thickness, column shape/size/orientation, span length, drop panel, concrete grade and reinforcement details/arrangements using non-linear FE analyses. To show the effeminacy of the proposed model, the ‘cradle-to-gate’ embodied carbon per unit floor area of the conventional and optimised system is calculated.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisors: Dr Mosleh Tohidi and Professor Ali B-Jahromi

Background

In moment-resisting frame systems, the consumed concrete in the slabs is around seven times that of columns. On the other hand, the depth of the two-way joist system is more than solid slabs. It is obvious that the reduction of concrete in slabs will lead to a considerable reduction of CO2 emission in RC structures. To do so, a nonlinear FE model is developed to calculate the nonlinear long-term deflection of slabs and the possibility of further reduction of embodied carbon using slabs with less thickness meeting the more relaxed code limitation. The possibility of reducing embodied carbon by providing optimised compressive reinforcement and a new reinforcement arrangement to further reduction of slab thickness allowed by the deflection criteria is also taken into account.

Aim

The aim of this research is to change deflection limits using nonlinear analysis to estimate the effect of more relaxed serviceability limits on design optimisation.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisors: Dr Mosleh Tohidi and Professor Ali B-Jahromi

Background

In all types of slabs, only 10-15% of the depth is under compression and contributes in strength and stiffness. In other words, a large amount of concrete in a tension zone is just used as a filler without any structural benefit, while significantly increasing the dead load and the seismic load. To end this, in the last decades various voided slabs with different brands have been proposed, but still concrete in the tension zone is considerable. In RC structures, concrete can effectively sustain compression stresses while reinforcement bars are the perfect element to sustain the tensile force.

Aim

The aim of this research is to develop a new floor system with compressive membrane action to eliminate concrete in most parts of the tension zone.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisors: Dr Mosleh Tohidi and Professor Ali B-Jahromi

Background

Flat slab and beam-slab system is widely used in the construction industry. In order to optimise the roof system, especially in spans greater than seven metres, the two-way joist system is considered as an economical solution. The two-way joist system consists of an integrated combination of regularly spaced joists and a top slab designed for two-way operation. Due to the non-use of precast advantages, the use of significant formwork (even heavier than the beam-slab system) and the forced implementation of a false ceiling, this system does not have a significant relative advantage in residential buildings with a conventional floor system. To do so, a new floor system is proposed using a nonlinear FE model to study the structural performance of the system through a comprehensive parametric study considering various boundary conditions and span lengths.  Furthermore, the possibility of slab with less thickness compared to the conventional two-way joist system is investigated.

Aim

The aim of this research is to develop a novel floor system with a new construction method to reduce concrete and eliminate heavy framework, in order to propose a more economical solution and reduce the CO2 emission.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisor: Dr Reza Keihani

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Overview

We are seeking a highly motivated and talented individual to undertake a PhD research project in the field of predictive maintenance in Concrete Structures using machine learning. This project will be supervised by Dr. Reza Keihani, an expert in structural engineering. The successful candidate will have the opportunity to contribute to cutting-edge research and address real-world challenges in infrastructure maintenance.

Research context

Concrete structures, such as bridges, buildings, and infrastructure, are essential components of our modern society. However, the deterioration of concrete over time due to various factors necessitates regular maintenance and repair to ensure their longevity and safety. Traditional maintenance practices are often reactive and can be costly and time-consuming. Therefore, there is a growing interest in leveraging advanced technologies, particularly machine learning and artificial intelligence (AI), to develop predictive maintenance strategies for concrete elements. This PhD research opportunity aims to explore the application of machine learning techniques in predicting maintenance requirements for concrete elements, leading to more efficient and proactive maintenance practices.

Candidate profile

Applicants will be expected to have:

• A first-class or upper second-class degree (1:1 or 2:1) in Structural Engineering or a related discipline

• Strong knowledge in Structural Engineering, particularly in Concrete Structures

• Familiarity with machine learning techniques and data analysis is desirable

• Commitment and enthusiasm for conducting research and pushing the boundaries of knowledge in the field

• Excellent analytical and problem-solving skills

• Strong communication and technical writing skills

UK and international students are eligible to apply for this PhD position

Further information

Questions regarding academic aspects of the project should be directed to Dr Reza Keihani: Reza.Keihani@uwl.ac.uk

Primary supervisors: Dr Mosleh Tohidi and Professor Ali B-Jahromi

Background

Intermediate moment frames (IMFs) are expected to withstand limited inelastic deformations in their members and connections when subjected to the forces resulting from the seismic loads. Previous studies have highlighted that ductility and continuity play a significant role in preventing progressive collapse and avoiding brittle failure. The majority of those studies have been conducted on the progressive behaviour of RC or steel frame structures, while research on the progressive collapse of moment-resisting frames with a voided flat slab is limited. Due to the flexibility of the layout, lower story height and faster construction process, it is widely used in the UK industry and worldwide. It is obvious that the progressive collapse behaviour of moment-resisting frame with a voided flat slab is different compared to that of column-beam frame structure. Especially, punching shear failure in the voided flat slabs needs to be taken into account in overall structural behaviour, which is not the case for other structural systems.

Aim

This study is focused on the progressive collapse behaviour of the intermediate moment frames with voided flat slab following various column removal scenarios, especially the potential of punching shear failure using non-linear FE analyses. Furthermore, a parametric FE study is conducted considering various structural layouts, concrete grades, column size/shape/orientation and reinforcement arrangements.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisors: Dr Mosleh Tohidi and Professor Ali B-Jahromi

Background

Special moment frames (SMFs) are expected to withstand significant inelastic deformation during a design earthquake, so special proportioning and detailing requirements are therefore essential to resist strong earthquake shaking. It is generally agreed that ductility and continuity are the two most important factors to prevent progressive collapse. A special moment-resisting frame is designed to provide the highest ductility and continuity compared to the other structural systems. This system is used in areas with a high risk of earthquakes or in high-rise buildings. Due to these types of RC structure being designed for seismic load with high ductility, the existing reinforcement bars is significantly more than required under load combination of gravity loads which is applied on the structures to analyse and design for progressive collapse.

Aim

In this study, the possibility of providing a safe alternate load path without using any further reinforcement bars to bridge overload from the damaged part to the adjacent elements in the special moment-resisting frame with a voided slab is investigated following various column removals scenario using non-linear FE analyses.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisor: Dr Spyridon Paschalis

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

The safety of masonry structures is of high importance. Recent accidental actions and catastrophic events have highlighted the insufficiency of existing masonry structures. This can affect people’s lives and can also have an economic impact. The cost of lost property, expenses from medical care through injury, lost income from damaged buildings or expenses if relocation is necessary are some examples of the economic impact.

Nowadays, there are available repair and strengthening techniques that have been proved insufficient. The extensive preventative application of these techniques for the protection of existing masonry structures cannot also be applied mostly due to issues linked to cost and difficulty in the application. The present research focuses on the application of novel materials and techniques for the repair and strengthening of masonry structures.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and/or a Masters degree (or equivalent) in Civil Engineering, Structural Engineering. Confidence in Finite Element packages will be advantageous. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives.

Applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Spyridon Paschalis: Spyros.Paschalis@uwl.ac.uk

Primary supervisor: Dr Spyridon Paschalis

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

Safety of structures is of high importance affecting people’s lives. At present, there are many old structures that were designed without any regulations or are based on those which have proved to be inadequate. In addition, a large number of structures built in the past have reached the end of their service life and in many cases are used for a different purpose to their original design specification.

The safety of structures is a crucial issue in high seismic risk areas. Existing structures may have undergone an earthquake with unknown effects. The effects of an earthquake can be devastating in many ways. Hence, earthquakes can affect peoples’ lives and can also have an economic impact. The cost of lost property, expenses from medical care through injury, lost income from damaged buildings or expenses if relocation is necessary are some examples of the economic impact from an earthquake.

Finally, structural safety is an important issue for existing structures that have been submitted to accidental actions during their service life and in which case their load-carrying capacity system needs to be upgraded.

The structural upgrade of existing structures is a key priority worldwide. Nowadays, there are many available strengthening techniques. However, the extensive preventative application of these techniques for the protection of existing structures cannot be applied mostly due to issues linked to difficulties during the application of the techniques, which require special expertise, increased cost and construction time.

The present research focuses on the development of novel techniques for strengthening of structures using advanced materials which combine good mechanical properties, sustainability, cost-effectiveness and ease of production.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and/or a Masters degree (or equivalent) in Structural Engineering or Civil Engineering. Confidence in Finite Element packages will be advantageous. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives.

Applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Spyridon Paschalis: Spyros.Paschalis@uwl.ac.uk

Primary supervisors: Dr Mosleh Tohidi and Professor Ali B-Jahromi

Background

According to most national and international codes of practices, the response modification factor of moment-resisting RC frames with a flat slab is less than frames with a beam-slab system by 50%. This value is highly dependent on the beam to slab stiffness ratio which has not been addressed by codes of practices.

In the system with beam-slab stiffness ratio of 2.0 and more, 94% of gravity loads are transferred directly from the slab to the beams. Furthermore, FE analyses indicate that, in these systems, bending moment in the columns due to lateral loads is distributed between beams interring the connection, and only a negligible part of this bending moment is transferred to the slab. It means that for the beam-slab system with a beam/slab stiffness ratio of more than 2.0, the slabs do not need to be designed for lateral load. In this case, the slab stiffness can be considered in the lateral stiffness of the frame.

It is obvious that, in the moment-resisting frame with a flat slab, the bending moment due to lateral loads is directly transferred to the flat slab, hence in the seismic areas moment-resisting frame with flat slab is recommended only for the RC structures up to three storeys. In structures with more storeys, to sustain seismic loads, shear walls need to be used. In this system, the contribution of the flat slab in lateral stiffness is ignored.

In the frame system with beam-slab stiffness ratio of less than 2.0, depending on the relative stiffness of beam to slab, a considerable part of the bending moment, due to lateral loads, is directly transferred to the slab. It means that, depending on the beam stiffness, frames behave between frame with flat slab and frame with beam-slab having beam/slab stiffness of 2.0 or more, hence the response modification factor need to be varied for different beam/slab stiffness ratio or, conservatively, response modification factor of the flat slab with shear wall need to be applied.

Some designers ignore the beam/slab stiffness ratio, consider a constant response modification factor for frame systems with any beam/slab stiffness ratio and also consider the slab stiffness in the lateral stiffness of the frame for both ULS and SLS stage, even for the beams with a depth of only 10% more than slab's depth.

Aim

The aim of this study is to investigate the effect of beam/slab stiffness ratio on the response modification factor and study the limitation of considering the stiffness of the slab in the lateral stiffness of the structure.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisors: Dr Mosleh Tohidi and Professor Ali B-Jahromi

Background

Significant studies have been conducted on different structural systems under lateral loads. Recently, shear walls with special details have been subjected to more research.

In highly seismic areas to resist lateral loads, a steel plate shear wall system is used. Unstiffened steel shear walls have been very popular in North American applications, while in Japan mostly stiffened shear walls are used.

The system designed with the steel shear walls is very ductile and has relatively large energy dissipation capability and due to high initial stiffness is able to limit the drift.

Plates have been used to increase the cyclic behaviour of steel shear walls, which help the middle plates to enter the non-linear phase faster and, as a result, the system has a much higher energy dissipation.

Aim

In this study, the strength of steel shear walls with various stiffener arrangements under the effect of earthquakes is experimentally investigated. Following the validation of the FE model, a comprehensive parametric study using various walls thickness, Stiffener's geometry and arrangements under cyclic loading is conducted.

Further information

Questions regarding academic aspects of the project should be directed to Professor Ali B-Jahromi: ali.bahadori-jahromi@uwl.ac.uk

Primary supervisor: Dr Spyridon Paschalis

Start dates: January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research context

Ultra-High Performance Fiber Reinforced Concrete (UHPFRC) is an advanced cementitious material with enhanced mechanical characteristics. The compressive strength of the material can reach values up to 200 MPa and the tensile strength up to 15 MPa. The superior mechanical properties of UHPFRC make the use of this material attractive for repair and strengthening applications.

An important benefit of UHPFRC is that thin UHPFRC layers with high strength and ductility can be constructed. Consequently, the geometry of the strengthened elements does not change dramatically. On the contrary, in other techniques, such as using Reinforced Concrete jackets or layers the geometry of existing elements changes dramatically. Another benefit is that for the preparation and the application of UHPFRC only simple tools need to be used, which can reduce the total time which is required for the application of the technique and also the total cost. Finally, another benefit of the examined technique is that due to the big volume of fibres in the mixture, the shrinkage of UHPFRC is less significant. In the present research, UHPFRC is applied for repair and strengthening applications and crucial parameters of the technique are investigated.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and/or a Masters degree (or equivalent) in Civil Engineering or Structural Engineering. Confidence in Finite Element packages will be advantageous. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives.

Applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Spyridon Paschalis: Spyros.Paschalis@uwl.ac.uk

Principle Supervisor: Dr Nagham Saeed 

Start dates:  January, May and September of each academic year 

Duration: Three-year position for full time and four-year position for part time

Research Context 

5G and beyond networks envision a massive increase in wireless network devices and traffic demands. This translates to significant growth in energy consumption, resulting in high network operation costs. Extending the usage of artificial intelligence (AI) and machine learning (ML) in all the network layers is a business target that may enhance the communication radio interface. Minimising network operation costs and energy consumption will help us to reach net zero goals.

Research goal

This research time works toward an energy-efficient wireless network. The aim is to study and evaluate AI and ML usage in the 5/6G network.

To achieve this, a thorough theoretical and analytical investigation should be done to evaluate current wireless networks, focusing on their architecture prior to identifying the main feature to use these optimisation techniques. The proposed design model should be simulated and tested using professional software and/or a testbed. 

Candidate profile 

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Electrical and Electronic Engineering or other similar disciplines. Programming skills with Python, MATLAB, C/C++ or any equivalent software will be considered favourably. The candidate should be able to work in a collaborative environment with a strong commitment to reaching research excellence and achieving assigned objectives. 

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Dr Nagham Saeed: Nagham.Saeed@uwl.ac.uk

Primary supervisor:  Dr Nagham Saeed 

Start dates:  January, May and September of each academic year 

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research Context 

The evolution of Internet of Things (IoT) and the massive number of connected devices is paving the way towards the smart cities revolution. The smart city systems include smart healthcare, smart education and smart transportation communicating together and sharing their data. The criticality of the data and access control challenges fostered the demand of using blockchain technology as an effective solution due to its immense security, decentralisation feature, immutability and traceability. Although the integration between the blockchain and IoT system already solved the major security problem facing the smart cities, the overall architecture needs some enhancements and modifications to ensure the maximum throughput, minimum transaction delay and utmost scalability.

Research goal

The main aim of the project is to design and develop a novel communication protocol for IoT devices that have adopted blockchain technology, aiming to improve the scalability and latency issues in the smart cities' communication systems with the benefit of being secured using blockchain technology.

To achieve this, a comprehensive theoretical and analytical investigation should be done to evaluate similar proposed algorithms, focusing on their architecture prior to designing a novel lightweight communication algorithm for Blockchain-IoT Based systems. The proposed design model should be simulated and tested using MATLAB or other available software tools. 

Candidate profile 

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Electrical and Electronic Engineering or other similar disciplines. Programming skills with MATLAB, C/C++ will be considered favourably. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work. 

Further information 

Questions regarding academic aspects of the project should be directed to Dr Nagham Saeed: Nagham.Saeed@uwl.ac.uk 

Primary supervisor:  Dr Nagham Saeed 

Start dates:  January, May and September of each academic year 

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research Context 

Smart transportation and automotive revolution were the product of the development and research in the area of Vehicular Ad hoc Networks (VANETs). This smart transportation is the pathway to the development of the next level of Intelligent Transportation Systems (ITSs). Hence, there is a thriving demand for wireless connectivity between vehicles and their environments (both internal and external). Such interactions could be vehicle-to-sensor on-board (V2S) communication, vehicle-to-device (V2D) communication, vehicle-to-pedestrian (V2P) communication, vehicle-to-vehicle (V2V) communication, vehicle-to-road infrastructure (V2R) communication, vehicle-to-network (V2N) communication, vehicle-to-grid (V2G) communication and/or vehicle-to-internet (V2I) communication. The provision of various wireless vehicle connectivity requires effective and efficient protocols with cutting-edge communication proficiencies.

Research goal 

The main aim of the project is to design and develop a novel communication protocol that could be used for different communication platforms and improve the quality of service for smart transportation.

To achieve this, a thorough theoretical and analytical investigation should be done to evaluate the communication protocols provided for autonomous vehicles prior to a comprehensive simulation for various VANET protocols that provide services such as V2I, V2V, V2D, V2S and V2G. The finding from the study and the simulation will contribute to designing a novel V2X communication algorithm for smart transportation (Integrated Communication Protocol). The available protocols and the proposed design model should be simulated and tested using MATLAB or other available software tools. 

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Electrical and Electronic Engineering or other similar disciplines. Programming skills with MATLAB, C/C++ will be considered favourably. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. 

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work. 

Further information 

Questions regarding academic aspects of the project should be directed to Dr Nagham Saeed: Nagham.Saeed@uwl.ac.uk 

Primary supervisor:  Dr Nagham Saeed 

Start dates:  January, May and September of each academic year 

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research Context 

Vehicular Ad-hoc Networks (VANETs) have been gaining significant attention from the research community due to their increasing importance for building an intelligent transportation system. The characteristics of VANETs, such as high mobility, network partitioning, intermittent connectivity and obstacles in city environments make routing a challenging task. Due to these characteristics of VANETs, the performance of a routing protocol is degraded. Currently, in any Vehicle Ad Hoc Network (VANET), a specific V2V routing protocol routes the packets to their destination no matter what the network's context is. This 'one size fits all' approach is far from optimum.

Research goal 

The main aim of the project is to introduce a novel intelligent V2V routing protocol selector for VANET. The intelligent selector learns the network's context then chooses the optimum routing protocol accordingly such as high mobility, network partitioning, intermittent connectivity and obstacles in city environments.  

To achieve this, a thorough theoretical and analytical investigation should be done to evaluate V2V routing protocols prior to a comprehensive simulation for various VANET protocols that provides V2V services. The finding from the study and the simulation will contribute to designing a novel V2V routing protocol selector. The selector recommends the optimum network context depending on the current network situation. The selector is adaptable to the variations in the network environment with ML prediction indicating the changes in the network context. The available protocols and the proposed design system model should be simulated and tested using MATLAB or other available software tools. 

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Electrical and Electronic Engineering or other similar disciplines. Programming skills with MATLAB, C/C++ will be considered favourably. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. 

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information 

Questions regarding academic aspects of the project should be directed to Dr Nagham Saeed: Nagham.Saeed@uwl.ac.uk 

Primary supervisor:  Dr Nagham Saeed 

Start dates:  January, May and September of each academic year 

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research Context 

Usually, the power microgrid corresponds to the coordinated operation of a cluster of loads, distributed generators and energy storage systems. This is quite appealing due to its flexibility, controllability and energy management capabilities. To provide an uninterruptible power supply to the loads, microgrids are expected to operate in both grid-connected and stand-alone modes and economically meet the demand on an instantaneous basis. To provide an efficient service, a secure transaction for the microgrid resources is required.

The issue of secure and reliable energy distributed into microgrids and large power grids have recently gained considerable attention. The criticality of the data and access control challenges fostered the demand of using blockchain technology as an effective solution due to its immense security, decentralisation feature, immutability and traceability.  

Research goal 

The main aim of the project is to design and develop a novel secure and efficient energy distribution transaction system for microgrid based on blockchain technology, aiming to improve some parameters in the process such as latency issues.   

To achieve this, a thorough theoretical and analytical investigation should be done to evaluate similar proposed algorithms, focusing on their architecture prior to designing a blockchain-based energy distribution transaction system for microgrids systems. The proposed design model should be simulated and tested using MATLAB or other available software tools.

Candidate profile 

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Electrical and Electronic Engineering or other similar disciplines. Programming skills with MATLAB, C/C++ will be considered favourably. The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. 

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information 

Questions regarding academic aspects of the project should be directed to Dr Nagham Saeed: Nagham.Saeed@uwl.ac.uk 

Principal Supervisor: Dr Abel Yeboah-Ofori

Supervisory Team: Dr Abel Yeboah-Ofori

State Dates: January, May and September of each year

Duration: Full-time 3 years or part-time 5 years

Research Scope and Aim

Cyber Physical Systems (CPS) and Cyber Supply Chain (CSC) security have increased exponentially as threat actors are deploying various attack methods including Advanced Persistent Threats (APT) to penetrate, infiltrate and manipulate these critical infrastructures. Its cascading impact on major organisations and nation-states and their cascading impacts on critical infrastructures, industrial control systems, assets and data leading to major financial loss, human losses and other cyber threats are phenomenal.

A cyber security breach survey report 2022 by the Department for Digital, Culture, Media and Sports (DCMS) indicated the need for the study of cyber security, as 39% of UK businesses identified cyberattacks. The report highlighted that almost all organisations have some form of digital exposure, with over nine in ten businesses (92%) and eight in ten charities (80%) having digital services and processes online. An ITPro report 2019 indicated that 50% of today’s cyberattacks are using island hopping attacks on CSC systems. Thus, the increase in cyberattacks and cybercrimes on industries, organisation and nation states.

Challenges

Factors leading to the challenges of addressing such attacks are due to the invincibility nature of cyberattacks, the dynamic nature of data in transit especially in live systems as well as the fuzzy nature of digital data that makes it indistinct, vague and difficult to perceive during cyberattacks and digital forensics analysis.

To address these challenges, a cyber resilience approach is required to mitigate such attacks, cyber threat intelligence gathering is required to understand the threat landscape, and deep learning methods are required to predict the know-unknow and unknown-unknow attacks.

The school is welcoming PhD candidates that are interested in the following areas:

• Cyber Physical Systems Security and Cyber Resilience
• Cyber Supply Chain Security and Threat Predictive Analytics
• Industrial Control Systems Security and Attack Propagation
• Digital Forensics and Cyber Criminology
• Cyber Threat Predictive Analytics using Deep Learning
• Cyber Threat Intelligence Gathering in Advance Persistent Threat using AI
• Cyber Security in IoT Devices and AI
• Intrusion Support Systems in Critical Infrastructure Systems Resilience
• Cyber Security in Financial Technology (FINTECH) Operational Technology and Critical Infrastructure Systems
• Phishing Detection for Fintech: Enhanced Fraud Detection for AI-Generated Phishing and Deepfake Social Engineering
• Critical Infrastructure Security and Digital Twin Interoperability Systems using AI 

Other PhD subject areas of interest in Cyber Security and Digital Forensics are welcomed.

Candidate Profile

The candidate must have an MSc degree in Computer Science, Cyber Security or IT degree background, with theoretical and technical knowledge of computer networks, strong software development skills coupled with deep learning (nonessential). Industry knowledge and experience are good but not essential.

The candidate must possess a strong desire and commitment to reaching research excellence with the aim of developing innovative ideas. As a self-starter with strong analytical skills, logical reasoning, perception skills and thought processes required to in establishing theoretical frameworks and understand the cyber security, threat intelligence and cyberattack methods.

Applicants who have no is not their first language must demonstrate their English language proficiency through evidence of IELTS at an overall level.

Further Information

Further questions regarding the subject areas and academic aspects should be directed to Dr Abel Yeboah-Ofori: abel.yeboah.ofori@uwl.ac.uk

Supervisory team: Professor Wei Jie

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research. This PhD position is based in the School of Computing and Engineering.

According to research firm Gartner Big Data, analytics will play a crucial role in cybersecurity. Cybersecurity analytics with Big Data will let organisations sift through massive amounts of security-related data – generated inside and outside the organisation – to uncover hidden relationships, detect patterns and remove security threats. This will enable organisations to see a bigger and broader picture of the security landscape for their organisations. Cybersecurity analytics with Big Data are applicable in many security use cases such as network monitoring, authentication and authorisation of users, identity management, fraud detection and systems of governance, risk and compliance. This new technology will change also the nature of such cybersecurity controls as conventional firewalls, anti-malware and data loss prevention. 

The information needed to uncover security events loses value over time and timely intelligent data analytics is critical as cyber criminals move much more quickly to commit their attacks. Therefore cybersecurity analytics must blend real-time analytics on data in motion with historical analysis on data at rest. By deploying security-specific analytics, organisations can find new associations or uncover patterns and facts. This real-time insight can be invaluable for detecting new types of threats as well.

Research goal

In this project, we will work on real-time cyber-attack prediction and mitigation solutions leveraging Big Data analytics, in order for organisations to detect new threats early and react quickly before they propagate. The School works closely with world-class industrial partners (eg SEGA Europe Ltd, Amazon UK) to drive this project with real-world enterprise security scenarios. More specifically, this project aims to:

• Design innovative algorithms and a model for real-time cybersecurity analytics to detect anomalies and abnormal behaviours immediately. Huge volumes of Big Data from diverse sources need to be observed, analysed and visualised in real-time manner to achieve advanced predictive capabilities and automated controls
• Develop a software tool that implements the proposed algorithms and model, in particular, based on open source large-scale Big Data processing platform (eg Apache Hadoop and its ecosystem)
• Demonstrate and evaluate the cybersecurity analytics tool on Amazon Cloud. Experiments will be conducted to benchmark the performance of the developed algorithms and model

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science/Cybersecurity and combine solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data, and conclude with the validation of a proposed solution through real applications/case studies.

Background knowledge and/or previous experience in the following areas/technologies will be considered very favourably: 

• Cybersecurity knowledge and skills
• Big data processing and analytics
• Cloud computing architecture, infrastructure and solution design

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Wei Jie: wei.jie@uwl.ac.uk

Primary supervisor: Professor Wei Jie

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering.

Internet of Things (IoT) is a part of future Internet and comprises many billions of Internet Connected Devices (IDOs) or ‘things’ where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/virtual identities and attributes. IDOs can include sensors, RFIDs, social media, business transactions, smart consumer appliances, lab instruments, etc. The vision of IoT is to allow ‘things’ to be connected anytime, anyplace, with anything and anyone, ideally using any path, any network and any service. This IoT vision has recently give rise to the notion of IoT Big Data applications that could produce billions of data streams and Zeta byte of data to provide the knowledge required to support timely decision making.  Some emerging IoT Big Data applications include emergency situations awareness, smart manufacturing, customer sentiment analysis, credit card fraud detection, remote sensing image processing and so on.

IoT Big Data applications need to process and manage streaming and multi-dimensional data from geographically distributed data sources. All these data sources are available in different formats, present in different locations and reliable at different confidence levels.

A number of issues need to be addressed in the implementation of next-generation IoT Big Data streaming processing model and associated techniques, in particular:

• Dynamic data fusion for heterogeneous and multi-source Big Data streams:
  The ability to make sense of data by fusing it into new knowledge is a critical requirement of IoT Big Data applications. Therefore multi-source data fusion is a very important area of current research. The key aspect in data fusion applications is the appropriate integration of all types of information or knowledge.
• Autonomic Cloud resource provisioning and configuration for IoT Big Data streaming:
  While data centre Clouds offers an abundance of resources, they do not offer any support for performance-driven autonomic provisioning and configuration of resources in response to changes in the volume, variety and velocity of IoT Big Data streams. It is essential to develop techniques for data centres to monitor and predict the requirement / behaviour of IoT Big Data streaming, and automate Cloud resource provisioning and configuration to meet QoS needs. 

Research goal

We aim to develop an IoT Big Data stream processing framework in data centre Clouds. More specifically, the project aims to:

• Develop an innovative approach for diffusion and processing (detection, collection, storage, extraction and reporting) of streaming IoT Big Data from multiple sources
• Implement a software framework that will enable autonomic provisioning and configuration for IoT Big Data application over Cloud resources
• Demonstrate the IoT Big Data stream processing framework on real applications (eg customer sentiment analysis) and deploy them on private and public Clouds as IoT SasS applications

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science and combine solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data, and conclude with the validation of a proposed solution through real applications/case studies.

Background knowledge and/or previous experience in the following areas/technologies will be considered very favourably: 

• Big data storage and processing
• Cloud computing architecture, infrastructure and solution design
• Data fusion techniques
• Storage, Data and Analytics Clouds
• High Performance Cloud Computing
• Cloud applications and experiences

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Wei Jie: Wei.Jie@uwl.ac.uk

Primary supervisor: Professor Wei Jie

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research. This PhD position is based in the School of Computing and Engineering.

A network is a natural data structure for organising data that has complicated interconnections. Many real word network systems can be modelled as a graph. With dramatic increases in the scale, dynamics and complexity of networks across many domains (eg the internet, social networks, biological networks, transportation networks), research on graph analytics becomes more challenging. Meanwhile, this research creates more profound impacts in various fields and many aspects of daily life. This project focuses on developing graph analytics theories, models and systems to address these challenges using machine learning technologies, employing them into wider application domains and driving the creation of software products.

Research goal

This project aims to develop algorithms and software tools for large-scale social network analysis. More specifically, the project aims to:

• Design innovative algorithms and models for large-scale graph analytics with machine learning
• Develop software tools that implement the proposed algorithms and model, ideally, parallel and distributed algorithms based on open-source big data processing platforms
• Demonstrate and evaluate the graph analytics tools on Clouds. Experiments will be conducted to benchmark the performance of the developed algorithm and tool

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science and combine a solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data, and conclude with the validation of a proposed solution through real applications/case studies.

Background knowledge and/or previous experience in the following areas/technologies will be considered very favourably:

• Big data storage and processing
• Cloud computing architecture, infrastructure and solution design
• Graph analytics algorithms and techniques
• Deep learning techniques

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Wei Jie: Wei.Jie@uwl.ac.uk

Supervisory team: Dr Ikram Rehman

Start dates: January, May and September of each academic year.

Duration: 3 years full-time or 5 years part-time.

Research scope and aim

The unprecedented population growth (currently from seven billion to over nine billion by 2050) is posing demand flux in terms of agricultural growth. The continuing globalisation and abruptly changing post-pandemic situation will expose the food systems to novel economic and political pressures. Hence, it becomes even more critical to adopt innovative ways and methodologies of producing agricultural yields for better productivity. At the heart of the monitoring operation in smart farming, modelling energy efficient IoT devices is vital in reshaping the conventional farming systems. Thanks to the enormous potential of renewable energy sources available within the environment, plenty of research dimensions evolve around designing low energy networking protocols to circumvent these power issues in smart farming systems that can significantly prolong network lifetime to make them “deploy and forget.” The frequent battery replenishment for a range of smart farming applications is infeasible because of their widespread span in the remote areas and countryside and these are an expendable source of carbon emissions with adverse environmental effects. Hence, energy scavenging sources (both ambient and dedicated) can be exploited to feed nodes in the environmental monitoring applications for smart farming making them a viable approach for higher agricultural yields.

Candidate profile

Applicants will be expected to hold a good first degree and a Masters degree in Computer Science or equivalent. Potential candidates are ideally expected to have demonstrated research experience in the field of IoT/IoV systems, strong grip in programming with Python/MATLAB and have strong interest in learning new concepts around energy management systems, machine learning, edge computing and big data analytics. Hands on experience of hardware design and configuration such as different LoRa, NB-IoT transceivers and development boards such as Lopy4, Pygate is a plus.

Questions regarding academic aspects of the project should be directed to Dr Ikram Rehman: ikram.rehman@uwl.ac.uk

Supervisory team: Dr Ikram Rehman

Start dates: January, May and September of each academic year.

Duration: 3 years full-time or 5 years part-time.

Research scope and aim

Road safety is always of paramount importance as it directly involves human lives. There were 24,470 killed or seriously injured casualties (KSIs) in reported road traffic accidents reported to the police, for the year ending June 2020. This is a statistically significant decrease of 11% compared to the year ending June 2019 (27,471). Thanks to the evolution of Internet of Vehicles (IoV), plenty of safety applications can help to improve the safety of onboard passengers taking advantage of the connected vehicles for robust communication across the network.

Internet of Vehicles is a network of vehicles equipped with sensors, software and the technologies that mediate between these with the aim of connecting and exchanging data over the internet according to agreed standards, which enables users to be better informed and make safer, more coordinated and 'smarter' use of transport networks. There are a plenty of applications onboard, including both safety and infotainment applications that have distinct set of requirements in terms of throughput, latency and data rate support for both V2V and V2I communication. Low Power Wide Area Networks (LPWAN) have recently appeared to be a promising communication paradigm when it comes to mobility of vehicles communicating with each other as a part of Internet of Vehicles for a number of safety applications with low-rate requirements, in addition to 5G based solutions suitable for infotainment applications with relatively higher data rate requirements.

Candidate profile

Applicants will be expected to hold a good first degree and a Masters degree in Computer Science or equivalent. Potential candidates are ideally expected to have demonstrated research experience in the field of IoT/IoV systems, strong grip in programming with Python/MATLAB and have strong interest in learning new concepts around energy management systems, machine learning, edge computing and big data analytics. Hands on experience of hardware design and configuration such as different LoRa, NB-IoT transceivers and development boards such as Lopy4, Pygate is a plus.

Questions regarding academic aspects of the project should be directed to Dr Ikram Rehman: ikram.rehman@uwl.ac.uk

Supervisory team: Dr Ikram Rehman

Start dates: January, May and September of each academic year.

Duration: 3 years full-time or 5 years part-time.

Research scope and aim

A report by the future cities digital catapult identified over 32,000 companies in the UK providing innovative technological solutions to the national Smart City (SC) market contributing £16bn and 400,000 jobs to the UK economy. Hence, the UK has the potential to be a future leader in this domain until 2030 delivering Smart City solutions up to 25% of the global demand. The SC concept exploits numerous benefits integrating ICT to a range of physical devices (eg sensors) connected with IoT networks to optimise operational efficiency. These tiny devices are mostly battery-powered in nature and the energy exhaustive operation in most of the SC use-cases causes to replenish the batteries after a certain period, which is both cost and labour intensive.

Energy management and smart storage techniques employed by these systems are clearly not enough to achieve desired performance levels. Designing and prototyping the energy harvesting-IoT devices with self-harvesting capabilities, energy-efficient system with sensor devices could be realised that are capable to last for tens of years (exploiting the harvesting potential from the surroundings) which are at the heart of sustainable smart city solutions to turn this dream of sustainable Smart Cities into reality.

Candidate profile

Applicants will be expected to hold a good first degree and a Masters degree in Computer Science or equivalent. Potential candidates are ideally expected to have demonstrated research

Experience in the field of IoT/IoV systems, strong grip in programming with Python/MATLAB and have a strong interest in learning new concepts around energy management systems, machine learning, edge computing and big data analytics. Hands-on experience of hardware design and configuration such as different LoRa, NB-IoT transceivers and development boards such as Lopy4, Pygate is a plus.

Questions regarding academic aspects of the project should be directed to Dr Ikram Rehman: ikram.rehman@uwl.ac.uk

Primary supervisor: Dr Alireza Esfahani

Supervisory team: NA

Start dates:  January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research Context

The convergence of Software-Defined Networking (SDN) and wireless communication technologies has ushered in a new era of networking paradigms, offering unprecedented opportunities and challenges for optimising network performance and security. This doctoral study explores SDN-enabled wireless networks, aiming to advance our understanding of their potential, limitations and transformative impact.

The primary objective of this research is to develop innovative solutions that enhance wireless networks' performance and security by harnessing SDN's capabilities. This involves investigating the dynamic orchestration of network resources, the management of Quality of Service (QoS) parameters and the robust handling of security threats in wireless environments.

Research goal

The study will delve into the following key areas:

1. SDN-Based Network Virtualisation: Analysing how SDN can facilitate efficient network virtualisation in wireless domains, enabling dynamic allocation of resources and optimising network efficiency
2. QoS Optimisation: Exploring SDN's role in dynamically managing QoS parameters, ensuring a high-quality user experience in wireless networks for applications such as IoT, video streaming and realtime communications
3. Security and Threat Mitigation: Investigating SDN-driven security mechanisms to proactively detect and mitigate various wireless network threats, including attacks on confidentiality, integrity and availability
4. Resource Management: Evaluating SDN's effectiveness in resource allocation, load balancing and energy efficiency in wireless environments

The findings are expected to inform future network architectures, providing the groundwork for more reliable, efficient and secure wireless communications, with potential applications spanning from smart cities to Industry 4.0 and beyond.

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science and combine solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data and conclude with the validation of a proposed solution through real applications/case studies.

Preference will be given to candidates with background knowledge and/or previous experience in the following areas and technologies:

• Wireless Communications
• Cloud Computing Architecture
• Software-Defined Networking (SDN)
• Cryptography

Additionally, proficiency in programming languages such as MATLAB and C/C++, as well as familiarity with simulation tools like NS3 and OMNeT, will be viewed favourably.

Applicants whose first language is not English must provide evidence of English language proficiency, either by achieving an overall IELTS score of 7 (with a minimum of 6.5 in all four language skills) or by providing access to chapters from their MA/MSc work or published research.

Further information

Questions regarding academic aspects of the project should be directed to Dr Alireza Esfahani: alireza.esfahani@uwl.ac.uk

Primary supervisor: Dr Alireza Esfahani

Supervisory team: NA

Start dates:  January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research Context

The intersection of Software-Defined Networking (SDN) and wireless communication has shown in a new era of networking possibilities, offering outstanding opportunities for optimising network performance and reliability. Within this context, network coding emerges as a promising technique that can revolutionise how data is transmitted, facilitating efficient utilisation of network resources and enhancing reliability. This doctoral study comprehensively explores network coding's potential in SDN-enabled wireless networks, aiming to advance its benefits, challenges and transformative impact. The primary objective of this research is to develop innovative network coding strategies tailored to the unique characteristics of SDN-enabled wireless environments.

Research goal

The study will investigate the following key areas:

1. Dynamic Resource Allocation: Analysing how network coding can be dynamically integrated into SDN controllers to optimise resource allocation, enhance data throughput and improve network efficiency
2. Reliability Enhancement: Exploring the role of network coding in increasing data reliability by mitigating packet loss and reducing latency, particularly in challenging wireless scenarios
3. Security Considerations: Investigating the security implications of network coding in SDN-enabled wireless networks and developing robust mechanisms to safeguard against potential threats
4. Scalability and Adaptability: Evaluating the scalability and adaptability of network coding solutions, considering factors such as network size, topology changes and varying traffic patterns

This study will employ a combination of theoretical analysis, extensive simulations and practical experimentation to assess the effectiveness of network coding in enhancing throughput and reliability in SDN-enabled wireless networks. The outcomes of this research are expected to provide valuable insights for network architects, operators and researchers, with potential applications spanning from next-generation mobile networks to Internet of Things (IoT) deployments, smart cities and beyond. Ultimately, this study aims to contribute to the evolution of more efficient, reliable and adaptable wireless networks in the era of SDN.

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science/Mathematics and combine solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data and conclude with the validation of a proposed solution through real applications/case studies.

Preference will be given to candidates with background knowledge and/or previous experience in the following areas and technologies:

• Wireless Communications
• Cloud Computing Architecture
• Software-Defined Networking (SDN)
• Cryptography

Additionally, proficiency in programming languages such as Python and C/C++, as well as familiarity with simulation tools like NS2 and OMNeT, will be viewed favourably. Applicants whose first language is not English must provide evidence of English language proficiency, either by achieving an overall IELTS score of 7 (with a minimum of 6.5 in all four language skills) or by providing access to chapters from their MA/MSc work or published research.

Further information

Questions regarding academic aspects of the project should be directed to Dr Alireza Esfahani: alireza.esfahani@uwl.ac.uk

Primary supervisor: Dr Alireza Esfahani

Supervisory team: Dr Shidrokh Goudarzi

Start dates:  January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research Context

In an era marked by rapid digital transformation, blockchain technology has emerged as a disruptive force with the potential to revolutionise industries, enhance transparency and decentralise data management. However, the adoption of blockchain has raised significant concerns regarding individual privacy, particularly in public blockchains where transaction data is inherently transparent and immutable. This research explores innovative strategies and technologies that balance blockchain's inherent transparency and the imperative need to safeguard user privacy. This multidisciplinary doctoral study will delve into the intersections of blockchain, privacy-enhancing technologies, cryptography and policy frameworks to address pressing challenges in privacy-preserving blockchain systems.

Research goal

The study will investigate the following key areas:

1. Privacy-Enhancing Mechanisms: Investigating and developing cryptographic techniques and privacy-preserving protocols to obfuscate transaction details while maintaining data integrity and auditability in blockchain networks
2. Zero-Knowledge Proofs: Exploring advanced zero-knowledge proof systems to enable private transactions and identity management on public blockchains
3. Decentralised Identity: Investigating decentralised identity solutions on blockchain networks ensures that users can securely control and manage their personal information
4. Regulatory Compliance: Analysing the evolving regulatory landscape surrounding blockchain and privacy and proposing compliance mechanisms and standards for privacy-preserving blockchain implementations

The outcome of this research will contribute to developing robust, privacy-preserving blockchain systems that respect individual privacy rights, comply with regulatory requirements and offer practical solutions for real-world use cases. The study will also provide insights into the evolving landscape of blockchain privacy, offering guidance for policymakers and industry stakeholders as they navigate the delicate balance between transparency and privacy in the digital age.

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science/Mathematics and combine solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data and conclude with the validation of a proposed solution through real applications/case studies.

Preference will be given to candidates with background knowledge and/or previous experience in the following areas and technologies:

• Wireless Communications
• Cloud Computing Architecture
• Blockchain
• Cryptography

Additionally, proficiency in programming languages such as Python and C/C++, as well as familiarity with simulation tools like NS2 and OMNeT, will be viewed favourably. Applicants whose first language is not English must provide evidence of English language proficiency, either by achieving an overall IELTS score of 7 (with a minimum of 6.5 in all four language skills) or by providing access to chapters from their MA/MSc work or published research.

Further information

Questions regarding academic aspects of the project should be directed to Dr Alireza Esfahani: alireza.esfahani@uwl.ac.uk

Primary supervisor: Dr Alireza Esfahani

Supervisory team: Dr Shidrokh Goudarzi, Dr Neda Azarmehr

Start dates:  January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research Context

The convergence of blockchain, machine learning and privacy represents a compelling frontier in the realm of digital innovation. While blockchain technology ensures data immutability and transparency, it also poses challenges to preserving user privacy when dealing with sensitive information. This doctoral research seeks to bridge the gap between these technologies by investigating novel approaches that enable privacy-preserving machine learning on blockchain networks.

Research goal

The primary goals of this interdisciplinary study include:

1. Privacy-Enhancing Blockchain Frameworks: Developing blockchain frameworks that incorporate advanced cryptographic techniques to protect user data while maintaining the integrity of transactions
2. Decentralised Machine Learning Models: Exploring decentralised machine learning models that operate directly on blockchain networks allows users to train and utilise AI models without exposing sensitive data
3. Differential Privacy: Investigating the integration of differential privacy mechanisms into blockchain and machine learning, ensuring that data analytics can be performed while preserving individual privacy
4. Homomorphic Encryption: Evaluating the applicability of homomorphic encryption in blockchain-based machine learning to enable secure data processing while data remains encrypted
5. Privacy-Preserving Smart Contracts: Designing privacy-preserving smart contracts that facilitate confidential transactions and interactions, particularly in applications involving financial data or healthcare records
6. Scalability and Efficiency: Addressing scalability and efficiency challenges to make privacy-preserving machine learning on blockchain practical for real-world applications

This research seeks to empower individuals and organisations with the ability to harness the power of machine learning while preserving their privacy in a blockchain-enabled world. The findings are expected to drive the development of privacy-centric blockchain applications in diverse domains, such as healthcare, finance and the Internet of Things (IoT), where data privacy is paramount.

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science/Mathematics and combine solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data and conclude with the validation of a proposed solution through real applications/case studies.

Preference will be given to candidates with background knowledge and/or previous experience in the following areas and technologies:

• Wireless Communications
• Blockchain
• Machine Learning
• Cryptography

Additionally, proficiency in programming languages such as Python and C/C++, as well as familiarity with simulation tools like NS2 and OMNeT, will be viewed favourably. Applicants whose first language is not English must provide evidence of English language proficiency, either by achieving an overall IELTS score of 7 (with a minimum of 6.5 in all four language skills) or by providing access to chapters from their MA/MSc work or published research.

Further information

Questions regarding academic aspects of the project should be directed to Dr Alireza Esfahani: alireza.esfahani@uwl.ac.uk

Primary supervisor: Dr Alireza Esfahani

Supervisory team: Dr Shidrokh Goudarzi, Dr Neda Azarmehr

Start dates:  January, May and September of each academic year

Duration: 3 years for full-time PhD or 5 years for part-time PhD

Research Context

The Internet of Vehicles (IoV) represents a transformative paradigm with great promise for enhancing transportation efficiency, safety and sustainability. As IoV systems collect vast amounts of data for optimising traffic flow, vehicle performance and safety measures, data privacy and sustainability concerns have gained prominence. This doctoral research aims to advance the state of knowledge in IoV by investigating sustainable and privacy-conscious machine-learning techniques tailored to IoV environments.

Research goal

The primary objectives of this interdisciplinary study encompass:

1. Sustainable Data Handling: Developing sustainable data collection and processing methods that reduce the environmental impact of IoV data-intensive operations while maintaining data quality and accuracy
2. Privacy-Preserving Machine Learning: Investigating privacy-preserving machine learning models and algorithms designed explicitly for IoV data, enabling data analysis without compromising individual privacy
3. Edge Computing for Sustainability: Leveraging edge computing in IoV for efficient data processing, reducing latency and conserving energy compared to centralised cloud-based solutions
4. Blockchain for Data Transparency and Trust: Assessing the use of blockchain technology for secure, transparent and auditable data sharing within IoV ecosystems while protecting data privacy
5. Energy-Efficient IoV Networks: Designing and optimising energy-efficient communication protocols and network architectures for IoV systems to minimise power consumption

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science/Mathematics and combine solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data and conclude with the validation of a proposed solution through real applications/case studies.

Preference will be given to candidates with background knowledge and/or previous experience in the following areas and technologies:

• Wireless Communications
• Cloud Computing Architecture
• Machine Learning
• Cryptography

Additionally, proficiency in programming languages such as Python and C/C++, as well as familiarity with simulation tools like NS2 and OMNeT, will be viewed favourably. Applicants whose first language is not English must provide evidence of English language proficiency, either by achieving an overall IELTS score of 7 (with a minimum of 6.5 in all four language skills) or by providing access to chapters from their MA/MSc work or published research.

Further information

Questions regarding academic aspects of the project should be directed to Dr Alireza Esfahani: alireza.esfahani@uwl.ac.uk

Primary supervisor: Dr Stephen Uzor

Start dates: January, May and September of each academic year.

Duration: This is a three-year position.

Research context

Collaboration is necessary in a wide variety of situations, for instance, education, design, the workplace and engineering, to name a few. Previous work has shown that extended reality (XR) technologies can enhance remote collaboration by promoting a sense of presence in users in virtual environments, for example, remote AR-to-AR or VR-to-VR collaboration. However, there is a lack of work in cross-reality collaboration, ie AR-to-VR collaboration. Particularly, it would be interesting to investigate how cross-reality collaboration can support tasks in dynamic environments. An example of a collaborative task, in this regard, is enabling virtual communication and interaction between patients and therapists in remote therapy sessions. Note: This concept may be adapted to different specialist areas if a reasonable scenario is proposed.

Research goal

The research will seek to answer the overarching question: How can dynamic environments support cross-reality collaboration between AR and VR users?

Methods

The project will use a mixed-methods approach, including quantitative and qualitative methods. An iterative design approach will be adopted using requirements gathering and formative and summative usability studies.

Tools and equipment

Advanced immersive technologies, for instance, augmented reality (head-mounted and mobile, eg HoloLens 2 and Smartphone) and untethered virtual reality tools (eg Meta Quest 3) will be utilised and complemented by machine learning.

Candidate profile

Applicants will be expected to hold a good undergraduate degree (first or upper second class) and a Masters degree (or equivalent) in Computer Science, Augmented or Virtual Reality, Game Design, Machine Learning or other similar disciplines. The candidate should be able to work in a collaborative environment, with a strong commitment to achieving research excellence and assigned objectives. It is expected that the PhD candidate will carry out applied research work that will start from established theoretical frameworks, continue with the implementation of new algorithms, methodologies and software for spatial and immersive technologies, experiment with real data and validate the proposed solution through real-life studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Questions regarding academic aspects of the project should be directed to Dr Stephen Uzor: stephen.uzor@uwl.ac.uk

Primary supervisor: Dr Omar Al Hashimi 

Start date: January, May, and September of each academic year 

Duration: 3 years full-time or 5 years part-time 

Research context

AquaFlux and Epsilon are two medical instruments that are designed and built by the BIOX lab at London South Bank University. They are used for human skin treatments by measuring skin wetness for cosmetic purposes. These medical instruments have now been commercialised and applied in more than 200 organisations worldwide. However, due to the nature of these medical devices, they often require on-site intensive training, which is expensive and time-consuming.

There is a genuine need for an interactive extended reality (XR) training environment that clients and trainees can access anytime from any location. The developed XR environment should serve as an effective tool for illustrating the features and functionalities of AquaFlux and Epsilon in a manner that is immersive, informative and engaging. 

Research scope

The aim of this PhD research is to develop an XR environment that leverages augmented reality (AR) and virtual reality (VR) technologies to create an immersive and interactive platform for illustrating the features and functionalities of these two medical instruments. This research project will encompass the following key areas:  

1. XR technology integration: Investigate the current state of XR technologies, including AR and VR, and assess their applicability for medical instrument visualization. Explore the hardware and software requirements for building an XR platform.

2. Medical instrument catalogue: Create a comprehensive catalogue of AquaFlux and Epsilon, demonstrating their full features and operations. Each instrument will be documented in terms of its specifications, components and usage. 

3. Content creation: Develop 3D models, animations and interactive elements to represent AquaFlux and Epsilon in a virtual environment. This will involve accurately portraying the physical attributes and functionality of each instrument. 

4. User interface and experience: Design an intuitive user interface for accessing and interacting with the XR environment. Implement user-friendly navigation and interaction techniques to facilitate engagement.

5. Interactivity and realism: Ensure a high degree of realism and interactivity within the XR environment, allowing users to fully interact with the developed XR environment.  

6. Educational applications: Explore the educational applications of the XR environment in medical training, medical simulations and healthcare education. Investigate its potential to improve understanding and retention of medical and research instrument knowledge.

7. Usability and user feedback: Conduct usability tests and gather user feedback to refine the XR environment. Identify areas for improvement and make iterative adjustments based on user input.

8. Accessibility and inclusivity: Address accessibility concerns to ensure that the XR environment is usable by individuals with varying abilities and that it caters to a diverse audience. 

Candidate profile

Applicants will be expected to hold a good undergraduate degree (first or upper second class) and a Masters degree (or equivalent) in Computer Science, Augmented or Virtual Reality, Game Design, Multimedia or other similar disciplines. The candidate should be able to work in a collaborative environment, with a strong commitment to achieving research excellence and assigned objectives. It is expected that the PhD candidate will carry out applied research work that will start from established theoretical frameworks, continues with the implementation of new algorithms, methodologies and software for spatial and immersive technologies, experiment with real data and validate the proposed solution through real-life studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at an overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Questions regarding academic aspects of the project should be directed to Dr Omar Al Hashimi: omar.alhashimi@uwl.ac.uk

Primary supervisors: Dr Stephen Uzor

Start dates: January, May and September of each academic year.

Duration: This is a three-year position.

Research context

Advancements in healthcare, and other factors, are allowing people to live longer. Nevertheless, there is a corresponding increase in health problems due to factors such as obesity, sedentary lifestyles and age-related degenerative conditions. For instance, a third of adults over the age of 65 years fall at least once a year. Diagnosing risk at an early stage is crucial to both the prevention of falls as well as informing a suitable intervention, thereby increasing confidence, reducing fear of falling and promoting independence in old age.

Gait retraining is necessary to improve mobility in these older adults. This project aims to design an intelligent extended reality (XR) system that can be used, by a clinician, to assess gait in users with mobility problems. The system would be essential to tracking recovery progress as well as informing and streamlining the decision-making process regarding future therapy.

Research questions

The research will seek to answer the following questions:

• What are the requirements for the design and development of an XR system to assess human gait in the community?
• Is XR effective for capturing human gait in real time?
• How can key gait parameters be effectively communicated to various stakeholders?

Methods

The project will use a mixed-methods approach, including quantitative and qualitative methods. An iterative design approach will be adopted using requirements gathering and formative and summative usability studies. A system will be developed using XR technologies to capture human gait in real time, using Unity, Unreal Engine or native development tools.

Tools and equipment

Advanced immersive technologies, for instance, augmented reality (head-mounted and mobile, eg HoloLens 2 and Smartphone) and untethered virtual reality tools (eg Meta Quest 3) will be utilised and complemented by machine learning.

Candidate profile

Applicants will be expected to hold a good undergraduate degree (first or upper second class) and a Masters degree (or equivalent) in Computer Science, Augmented or Virtual Reality, Game Design, Machine Learning or other similar disciplines. The candidate should be able to work in a collaborative environment, with a strong commitment to achieving research excellence and assigned objectives. It is expected that the PhD candidate will carry out applied research work that will start from established theoretical frameworks, continue with the implementation of new algorithms, methodologies and software for spatial and immersive technologies, experiment with real data and validate the proposed solution through real-life studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Questions regarding academic aspects of the project should be directed to Dr Stephen Uzor: stephen.uzor@uwl.ac.uk

Primary supervisor: Dr Livia Lantini

Start date: January, May and September of each academic year

Duration: 3 years for full-time PhD

Research context

In the landscape of urban development, it is imperative to consider the coexistence of natural and built environments. Trees in urban landscapes are essential for sustaining ecological integrity, optimising air quality and contributing to the physical and mental health of city inhabitants.

Nonetheless, the interaction between trees and civil engineering structures presents intricate challenges in ensuring both the health of trees and the structural integrity of buildings and infrastructure. The integration of non-destructive testing (NDT) methods, such as Ground Penetrating Radar (GPR), falling weight deflectometer (FWD), and others, offers a holistic approach to evaluate the dynamic relationship between tree root systems and civil engineering structures.

This research aims to establish a multi-faceted methodology, performing data fusion of established NDT methods, coupled with advanced data processing, analysis and visualisation. The aim of this project is to refine intervention strategies, enhance the integrity of civil engineering structures and assure the well-being of urban trees. Objectives include calibrating the precision of NDT methods to detect buried root patterns accurately, interpret complex datasets for root system analysis and creating sophisticated predictive models for pre-anticipating potential structural-root conflicts.

This innovative fusion will lead towards a new era of precision arboriculture and urban planning, where technology and nature harmoniously interface for the sustainable advancement of our cities.

Candidate profile

Applicants should possess:

• A Masters degree (or equivalent) in Civil Engineering, Environmental Engineering, Geophysics, Electrical and Electronic Engineering or other similar disciplines
• Strong knowledge in non-destructive testing methods, particularly in ground penetrating radar.
• Familiarity with artificial intelligence techniques is desirable
• Exceptional collaboration skills and a commitment to research excellence
• Proficiency in applied research, from theoretical framework establishment to algorithm development, data experimentation and real-life case study validation
• Proficiency in English (IELTS overall score of 6.5, not less than 6 in all four skills)

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Dr Stephen Uzor

Start date: January, May and September of each academic year

Duration: This is a three-year position

Research context

Effective monitoring of structural information is crucial in preserving the quality of civil structures and environmental assets and ensuring the safety of operations. Remote sensing (RS) data are crucial in this context and offer novel insights into this monitoring process.

Extended reality (XR) technologies enable people to visualise data in new, exciting and informative ways. With an additional depth layer, we can visualise digital versions of structures in a manner akin to real world observation. Immersive technologies can provide the following advantages for structural information in relevant industries:

• a) visualising existing accurate and multi-temporal structural information in 3D environments
• b) visualising predicted future structural risks and faults
• c) facilitating communication between stakeholders

However, a scaling problem exists to digitalise effectively sub-millimetre data from the satellite into observations that can be utilised in immersive environment.

Opportunities exist to publish the findings from this research in various publication disciplines, e.g. extended reality, computer science, civil engineering, cultural heritage and human-computer interaction.

Research goal

The main goal of the project is to develop an immersive extended reality system to visualise and predict structural integrity of civil and cultural heritage infrastructure and their interrelation with surrounding environmental assets (e.g. street trees, green areas) and using RS (e.g. Interferometric Synthetic Aperture Radar, Ground-Based SAR) data.

Methods

The project will use a mixed-methods approach, including quantitative and qualitative methods. An iterative design approach will be adopted using requirements gathering and formative and summative usability studies.

Tools and equipment

Advanced immersive technologies, for instance, augmented reality (head-mounted and mobile) and untethered virtual reality tools will be utilised and complemented by machine learning. Satellite imaging (high and medium resolution) data will be used to provide multi-scale measurements of sub-structural features, which will be fed into and processed by the proposed XR system.

Candidate profile

Applicants will be expected to hold a good undergraduate degree (first or upper second class) and a Masters degree (or equivalent) in Computer Science, Augmented or Virtual Reality, Game Design, Machine Learning or other similar disciplines.

The candidate should be able to work in a collaborative environment, with a strong commitment to achieving research excellence and assigned objectives. It is expected that the PhD candidate will carry out applied research work that will start from established theoretical frameworks, continue with the implementation of new algorithms, methodologies and software for spatial and immersive technologies, experiment with real data and validate the proposed solution through real-life studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 (not less than 6 in all four skills) or by providing access to MA/MSc chapters or published work.

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Professor Fabio Tosti

Start date: January, May and September of each academic year

Duration: This is a three-year position

Research context

Built Cultural Heritage (BCH) is a fundamental legacy that fosters a sense of community belonging which must be preserved. However, BCH is currently facing enormous pressure due to climate change, increasing urbanisation, mass tourism and human negligence (“Joint Programming Initiative on Cultural Heritage and Global Change - Strategic Research and Innovation Agenda” 2020).

Awareness has progressively raised about the need to shift from a curative towards a preventive approach to keep the cultural significance and the original concept of heritage assets. A preventive approach suggests intervening upstream by early detection and understanding the deterioration process through regular site monitoring and maintenance work, reducing the need for later interventions and associated costs.

There should be systematic and fast monitoring with temporal assessment for inclusion into dynamically updated computational models. In this regard, on one hand, Terrestrial Remote Sensing could play an important role in identifying inherent (e.g. natural frequencies and mode shapes) and surface (e.g. points cloud) structural information to incorporate into condition-based models and defect information systems of the heritage sites. On the other hand, the Digital Twin (DT) concept can be utilised as a dynamically updated asset-specific computational model(s) integrated within data-driven analyses and decision-making feedback loops.

Both technologies are at an early stage of development. However, it is believed that a more comprehensive exploitation of their potential and a synergistic use can pave the way to new paradigms in structural health monitoring, modelling and analysis of BCH.

Research goal

This project aims to develop a novel monitoring and assessment methodology for BCH. The approach will rely on condition assessment-based information collected using terrestrial remote sensing and integration with DT technology. This has the potential to drive policymaking into wider and more effective management.

To achieve this aim, the laser scanner and ground-based radar interferometry (GBIR) will be used to collect surface and inherent structural information, for incorporation into advanced models and DTs. The outcome will benefit the asset owners and other stakeholders to act on time to conserve the heritage.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Geodesy, GIS and Remote Sensing, Archaeology, Building Conservation, Civil Engineering, Electrical and Electronic Engineering or other similar disciplines. 

The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of new algorithms and methodologies and the experimentation with real data and conclude with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 (not less than 6 in all four skills) or by providing access to MA/MSc chapters or published work.

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Professor Fabio Tosti

Start date: January, May and September of each academic year

Duration: This is a three-year position

Research context

Ground Penetrating Radar (GPR) is undoubtedly the most versatile and powerful geophysical method for investigation of the subsurface geometrical and physical properties. Most of the processing of GPR data has been derived from seismic methods. This includes gain recovery, spiking deconvolution, bandpass filtering, velocity analysis, elevation and static corrections, f-k migration, amongst others.

On a broader scale, geophysical methods provide information on the physical properties of the subsurface, the spatial distribution of these properties and the structure of the subsurface. For this reason, in recent years the application of multi-dimensional geophysical techniques has become increasingly important for various geomorphologic problems.

Soil, ecosystems, and infrastructure systems are inherently 3D structures, often exhibiting small-scale spatial heterogeneity of subsurface conditions, and flow and transport processes in these systems tend to be complex both in space and time. Modern geophysical methods have the potential to provide spatial or even volumetric information on the subsurface internal structure and property variations of landforms, soils and ecosystems.

Because of the strong analogy between working principles of GPR and other geophysical methods (e.g. seismic) or the possibility to combine its capabilities under a multi-dimensional processing approach (e.g. joint inversion methods with magnetometry), interdisciplinary data processing is a fertile field of current research.

Research goal

This project aims to develop novel GPR-based data processing frameworks using capabilities of other geophysical methods. The approach will rely on condition assessment-based information of the subsurface internal structure of physical systems, that will be taken from real-life applications in the civil and environmental engineering and cultural heritage areas of science.

To achieve this aim, GPR systems of different frequencies will be utilised to collect subsurface information. The outcome will benefit new advancements in GPR data processing and the creation of new algorithms for practical applications in Earth sciences.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Geophysics or other similar disciplines.

The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of new algorithms and methodologies and the experimentation with real data and conclude with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 (not less than 6 in all four skills) or by providing access to MA/MSc chapters or published work.

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Dr Livia Lantini

Start date: January, May and September of each academic year

Duration: 3 years for full-time PhD

Research context

In the context of urban expansion, urban trees serve as crucial components of ecological balance, air quality enhancement, aesthetic appeal and resident wellbeing. However, maintaining harmony between these trees and urban infrastructure while mitigating emerging threats from pests and diseases, and protecting infrastructure from expanding root systems, poses significant challenges.

Ground Penetrating Radar (GPR) technology, as a non-destructive testing method, has shown promise in addressing these challenges, albeit with accessibility limitations. This project's primary goal is to establish an innovative approach by integrating GPR technology and artificial intelligence (AI) to achieve highly detailed mapping of urban tree roots. The ultimate aim is to enhance precision, efficiency and knowledge for managing tree roots effectively.

The specific objectives encompass developing innovative methods for urban tree assessment, advanced AI algorithms for autonomous tree root mapping using GPR data, and predictive models for root growth, real-time visualisation and proactive urban tree management through predictive analytics.

Candidate profile

Applicants should possess:

• A Masters degree (or equivalent) in Civil Engineering, Environmental Engineering, Geophysics, Electrical and Electronic Engineering or other similar disciplines
• Strong knowledge of artificial intelligence techniques
• Familiarity with non-destructive testing methods, particularly in ground penetrating radar, is desirable
• Exceptional collaboration skills and a commitment to research excellence
• Proficiency in applied research, from theoretical framework establishment to algorithm development, data experimentation and real-life case study validation
• Proficiency in English (IELTS overall score of 6.5, not less than 6 in all four skills)

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Dr Livia Lantini

Start date: January, May and September of each academic year

Duration: 3 years for full-time PhD

Research context

In the landscape of urban development, it is imperative to consider the coexistence of natural and built environments. Trees in urban landscapes are essential for sustaining ecological integrity, optimising air quality and contributing to the physical and mental health of city inhabitants.

Nonetheless, the interaction between trees and civil engineering structures presents intricate challenges in ensuring both the health of trees and the structural integrity of buildings and infrastructure. The integration of Non-Destructive Testing (NDT) methods, such as Ground Penetrating Radar (GPR), Falling Weight Deflectometer (FWD), and others, offers a holistic approach to evaluate the dynamic relationship between tree root systems and civil engineering structures.

This research aims to establish a multi-faceted methodology, performing data fusion of established NDT methods, coupled with advanced data processing, analysis and visualisation. The aim of this project is to refine intervention strategies, enhance the integrity of civil engineering structures and assure the well-being of urban trees. Objectives include calibrating the precision of NDT methods to detect buried root patterns accurately, interpret complex datasets for root system analysis and creating sophisticated predictive models for pre-anticipating potential structural-root conflicts.

This innovative fusion will lead towards a new era of precision arboriculture and urban planning, where technology and nature harmoniously interface for the sustainable advancement of our cities.

Candidate profile

Applicants should possess:

• A Masters degree (or equivalent) in Civil Engineering, Environmental Engineering, Geophysics, Electrical and Electronic Engineering or other similar disciplines
• Strong knowledge in non-destructive testing methods, particularly in ground penetrating radar.
• Familiarity with artificial intelligence techniques is desirable
• Exceptional collaboration skills and a commitment to research excellence
• Proficiency in applied research, from theoretical framework establishment to algorithm development, data experimentation and real-life case study validation
• Proficiency in English (IELTS overall score of 6.5, not less than 6 in all four skills)

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Dr Reza Keihani

Start date: January, May and September of each academic year

Duration: 3 years for full-time PhD

Research context

The escalating issue of reinforcement corrosion in concrete structures presents a formidable challenge in the construction industry, where the durability and safety of infrastructure are of paramount importance.

With a focus on early detection, the project seeks to intercept corrosion at its inception, bridging the gap left by traditional visual inspection methods which fall short in identifying initial corrosion stages. By leveraging the advanced capabilities of NDT and photogrammetry, the initiative drives us toward more accurate, non-destructive testing and assessment of subsurface defects. The project's significance lies in its potential to dramatically enhance infrastructure management, ensuring structural integrity and public safety while reducing maintenance costs and extending service life. 

Through rigorous validation process that investigates both simulated lab conditions and real-world structures, this project strives to influence future standards and practices within the construction industry.

Candidate profile

Applicants should possess:

• A Masters degree (or equivalent) in Civil Engineering, Geophysics, Structural Engineering, Environmental Engineering or other similar disciplines
• Familiarity with non-destructive testing methods, particularly in ground penetrating radar is desirable
• Exceptional collaboration skills and a commitment to research excellence
• Excellent time management and organisational skills for efficient work planning, meeting deadlines and maintaining regular communication with supervisors
• Strong knowledge in materials science, or a related field, demonstrating a robust understanding of structural integrity and material deterioration relevant to this project
• Proficiency in English (IELTS overall score of 6.5, not less than 6 in all four skills)

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Professor Fabio Tosti

Start date: January, May and September of each academic year

Duration: This is a three-year position

Research context

Effective health monitoring of railway sub-structural layers (ballast and sub-ballast) is crucial in preserving the quality of the track-bed and ensuring the safety of operations. When measuring the long-term integrity and functionality of the railway track, detection of early decay in track-bed foundation materials is vital.

Increasing traffic flow on rail network systems demands more time-efficient and vigorous techniques and methodologies in terms of safety and maintenance. The applications of non-destructive testing methods such as ground-penetrating radar (GPR) have been recognised and appreciated within the industry in recent years. In addition, use of remote sensing – such as ground-based interferometry, 3D laser scanners and satellite imaging – is proving strategic for the provision of multi-temporal and accurate information in the surface monitoring of structural displacements of transport infrastructures.

Investigations of the sub-structural layers of the track-bed by the proposing team have produced promising results that could potentially offer a more effective approach in railway foundation assessment and monitoring.

Research goal

The main target of the project will be to develop a novel railway ballast monitoring and assessment methodology.

To achieve this, a comprehensive set of laboratory-based experimental and fieldwork activities will be materialised. Some of the latest ground-based equipment – including GPR systems and other non-invasive techniques – will be utilised and complemented by extensive numerical simulations and modelling. Ground-based and satellite remote sensing (high and medium resolution) will be used to provide multi-scale measurements of structural features linked with the integrity of railway track foundations and ballast.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Civil Engineering, Transport Infrastructure Engineering, Geodesy, Geophysics, Remote Sensing, Electrical and Electronic Engineering, Aerospace Engineering or other similar disciplines.

The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that PhD candidates will carry out applied research work that will start with establishing a theoretical framework, continue with implementing new algorithms and methodologies and experimenting with real data, concluding with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 (not less than 6 in all four skills) or by providing access to MA/MSc chapters or published work.

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Professor Fabio Tosti

Start date: January, May and September of each academic year

Duration: This is a three-year position

Research context

Green infrastructure has a direct impact in our daily life. Good quality green infrastructure improves health, lifestyle and helps the mitigation of climate change at large. Urban woodlands are amongst the green infrastructures needing regular monitoring. Most of the monitoring is done by visual inspection and ground-based measurements. This makes it difficult to regularly monitor trees.

Remote sensing data can be used as a monitoring tool, since the satellite data has become available in high spatial and temporal resolution. Remote sensing has the potential to investigate estimation of the fundamental characteristics of trees, such as canopy size and shape, height and changes in the colour of the leaves, which are important parameters for tree health monitoring.

Research goal

The main aim of this project is to estimate canopy size and tree height using satellite remote sensing, and identify trees which are about to die or are already dead in large scale. The result can be integrated with available forest database and contribute towards regular monitoring of green infrastructure.

To achieve this aim, analysis of multi-temporal SAR and hyperspectral satellite data will be carried out to identify and estimate the characteristics and state of the trees. The outcome will enrich the current green infrastructure database and help the stakeholders to monitor efficiently the trees in urban settings.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Geodesy, GIS and Remote Sensing, Forestry, Electrical and Electronic Engineering, Aerospace Engineering or other similar disciplines.

The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that the PhD candidates will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of new algorithms and methodologies and the experimentation with real data and conclude with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 (not less than 6 in all four skills) or by providing access to MA/MSc chapters or published work.

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Professor Fabio Tosti

Start date: January, May and September of each academic year

Duration: This is a three-year position

Research context

Heritage conservation is a key player in sustainability planning. However, there are still unresolved issues between conservation aims and sustainability. The dynamics between urbanisation and heritage conservation should be considered as sustainable development agenda goals. The stability of the heritage assets and the effect of climate change and new developments are not fully explored and monitored. There should be a systematic monitoring in large scale with temporal assessment.

In this regard, Remote Sensing could play an important role in identifying building stability, ground subsidence and temperature change around the registered heritage sites.

Interferometric Synthetic Aperture radar (InSAR) is proven to detect sub-centimetre level detection of surface changes. The temporal and spatial resolution of SAR satellites are also becoming promising and very accurate for assets management.

Research goal

The aim of this project is to develop a novel monitoring and assessment methodology for heritage asset stability and conservation management systems. The approach will rely on condition assessment-based information collected using satellite imaging and integration with existing GIS systems. The observation will serve as an assessment and monitoring tool for heritage assets and will provide evidence based sustainable urban development endeavour. This has the potential to drive policymaking into wider and more effective management.

To achieve this aim, analysis of multi-temporal SAR images will be carried out to identify heritage assets stability and changes in state of conservation over time, categorise the severity based on the temperature change and subsidence rate. The cause of the subsidence will be analysed in relation to the new developments. Based on the deformation pattern, numerical models will be developed. The outcome will benefit the asset owners and other stakeholders to act on time to conserve the heritage.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Geodesy, GIS and Remote Sensing, Archaeology, Building Conservation, Civil Engineering, Transport Infrastructure Engineering, Pavement Engineering, Electrical and Electronic Engineering, Aerospace Engineering or other similar disciplines.

The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that PhD candidates will carry out applied research work that will start with establishing a theoretical framework, continue with implementing new algorithms and methodologies and experimenting with real data, concluding with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 (not less than 6 in all four skills) or by providing access to MA/MSc chapters or published work.

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Professor Fabio Tosti

Start date: January, May and September of each academic year

Duration: This is a three-year position

Research context

Highways and pavements become damaged because of decay or weakness in their structural components. An effective assessment of surface damage and the structural properties of pavement layers can identify the causes and locate the extent of the damage. The conventional methods of pavement damage assessment are very advanced but are limited in space and lack regular observations. Remote sensing observations could play a vital role in pavement damage assessment at the network level.

Earth Observation (EO) is becoming a standalone technology and an instrumental tool for developing new monitoring approaches. Interferometric Synthetic Aperture Radar (InSAR) have proven effective in assessing transport infrastructure conditions. The large-scale coverage of InSAR images allows the evaluation of large infrastructures at the network level in a systematic data processing workflow. The outcome of the SAR techniques integrated with the geographic information systems (GIS) give a unified information for pavement asset managers to further investigate at a local level.

Research goal

The aim of this project is to develop a novel monitoring and assessment methodology for inclusion in new-generation transport infrastructure smart management systems. The integrated approach will rely on condition assessment-based information collected using satellite imaging and integration with GIS systems. The observations will serve to prioritise interventions on highway infrastructure assets.

To achieve this aim, analyses of multi-temporal SAR data will be carried out to identify areas of concern at the network level, identify excessive deformation rate of the pavement surface and change detection using analysis of the coherence, are amongst others. This information will be instrumental to the local level investigation of other techniques to detect the source and scale of defects and deformations.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in Geodesy, GIS and Remote Sensing, Civil Engineering, Transport Infrastructure Engineering, Pavement Engineering, Electrical and Electronic Engineering, Aerospace Engineering or other similar disciplines.

The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that PhD candidates will carry out applied research work that will start with establishing a theoretical framework, continue with implementing new algorithms and methodologies and experimenting with real data, concluding with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 (not less than 6 in all four skills) or by providing access to MA/MSc chapters or published work.

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

Primary supervisor: Professor Fabio Tosti

Start date: January, May and September of each academic year

Duration: This is a three-year position

Research context

Development of urban areas can compromise green spaces. The harmonisation of green spaces with the built environment has a direct impact on our daily lives and in addressing mitigation of microclimate changes. One of the impacts of climate change is the dynamic characteristics of heat developed within cities, also known as "urban heat islands."

The spatio-temporal variation of surface temperature can be uncovered using low-to-medium spatial resolution thermal satellite images. To validate the surface temperature variation from satellites, we may use internet of things (IoT) sensors (e.g. thermal sensors). Remote sensing data can be used as a monitoring tool, since satellite data then becomes available in high spatial and temporal resolution.

Research goal

The main aim of the study is to determine the spatio-temporal variation of surface temperature in urban areas using thermal satellites and to develop data integration of in-situ measurements with the satellite results.

To achieve this aim, analysis of thermal and hyperspectral satellite data will be carried out to identify and estimate the land surface temperature (LST). The outcome will enrich the effort to understand the variation in LST within the urban environment and the effect of cooling of green spaces.

Candidate profile

Applicants will be expected to hold a good first degree (first or upper second class) and a Masters degree (or equivalent) in GIS and Remote Sensing, Forestry, Building Conservation, Electrical and Electronic Engineering or other similar disciplines.

The candidate should be able to work in a collaborative environment, with a strong commitment to reaching research excellence and achieving assigned objectives. It is expected that PhD candidates will carry out applied research work that will start with establishing a theoretical framework, continue with implementing new algorithms and methodologies and experimenting with real data, concluding with the validation of a proposed solution through real-life case studies.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 6.5 (not less than 6 in all four skills) or by providing access to MA/MSc chapters or published work.

Vice-Chancellor’s PhD scholarships

The University of West London is offering several opportunities for three year fully funded PhD scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international applicants.

Further information

Questions regarding academic aspects of the project should be directed to Professor Fabio Tosti (Director of the Faringdon Research Centre for Non-Destructive Testing and Remote Sensing): fabio.tosti@uwl.ac.uk 

PhD synopsis:

In the forward looking of Industrial 5.0, where the focus is on human-centric, human-empowered robotic AI, our groundbreaking PhD research project is set to shape the current state of generative AI, in the direction of generative robot AI, centres on robot learning and design, achieved through the exploration and development of diffusion models across various modalities for motion generation.

This research aims to establish a novel paradigm for robotic motion generation by bridging the gap between generative AI and reinforcement learning. This will be achieved through the seamless integration of diffusion models with reinforcement learning policies. This integration empowers robots not only to generate intricate motions but also to learn and adapt in real-time, offering flexibility and intelligence in dynamic environments. The result is the development of intelligent robotic systems capable of generating purposeful and context-aware actions in various scenarios.

We intend to develop multi-modal diffusion models to enable generative robotic motion using a variety of sensory inputs, such as microphones, cameras, depth sensing/lidar, etc. This approach encompasses text-to-action and observation-to-action, enabling robots to comprehend, adapt and execute actions based on diverse sources of information. This aligns seamlessly with the vision of adaptable, human-empowered robotic AI.

We aspire to make a meaningful contribution to the development of intelligent and adaptive robotic systems, steering in a new era of robotics in which humans and machines collaborate to achieve unprecedented levels of efficiency, safety and versatility.

The potential impact of this research extends from enhancing robot learning and motion planning to boosting productivity and design in various industrial and service sectors.

For information about this PhD opportunity, please contact Professor Jonathan Loo: jonathan.loo@uwl.ac.uk

Primary supervisor: Professor Jose Abdelnour-Nocera

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This particular proposal is focused on recognising the culturally diverse nature of digital communities and how to design useful technology for them.

In recent years, researchers in psychology and economics have increasingly called for a consideration of more diverse subject populations. Summarising contradictory findings between different human populations in various domains such as visual perception or analytic reasoning, Henrich et al (2010) observed that these research results are not broadly representative. In fact, findings in psychology are almost exclusively based on American undergraduates and other WEIRD (Western, Educated, Industrialised, Rich and Democratic) subjects.

Human-Computer Interaction researchers often build on these findings, thus designing technology that is optimised for WEIRD people. Moreover, a large majority of articles published at prominent HCI venues such as Interact or CHI reports on studies with WEIRD participants, ignoring that these results might not be replicable with other subject populations.

Research goal

The This PhD project is aimed at tackling two main questions:

• What are the obstacles faced by user researchers when replicating user studies with more diverse participants (eg in other countries and cultures)?
• What are major HCI principles that are currently being taken for granted but which are most likely not replicable across different countries and cultures?  

Candidate profile

The ideal candidate should have an MSc or equivalent degree with a strong human-computer interaction component and combine solid theoretical background and prototyping skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, so as ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with empirical work to explore the research goals and conclude with the validation of a proposed solution through prototype evaluations and user studies.

Background knowledge and/or previous experience in the following areas (in order of preference), though not mandatory, will be considered very favourably: Human-Computer Interaction, Social Sciences: Psychology and/or Sociology, Software internationalisation and localisation.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Jose Abdelnour-Nocera: jose.abdelnour-nocera@uwl.ac.uk

Primary supervisor: Professor Jose Abdelnour-Nocera

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This particular proposal is focused on working digital communities, eg telecommuters, mobile workers.

Human work analysis involves user goals, user requirements, tasks and procedures, human factors, cognitive and physical processes and contexts (organisational, social, cultural). In particular in the HCI and human factors tradition, work is analysed as end-user tasks performed within a work domain. The focus is on the user's experience of tasks (procedures) and the artefact environment (constraints in the work domain). Hierarchical Task Analysis (Annett & Duncan, 1967) and Work Domain Analysis (Salmon, Jenkins, Stanton & Walker, 2010) are among the methods that can be used to analyse the goal-directed tasks and map the work environmental constraints and opportunities for behaviour. In addition, there is a strong tradition in HCI for studying work with ethnographic methods (Button & Sharrock, 2009) and from sociotechnical perspectives (eg Abdelnour-Nocera, Dunckley & Sharp, 2007). These approaches focus on work as end-user actions performed together with other people in a field setting, that is, the user's experience of using systems are social and organisational experiences. User experience, usability and interaction design are influenced by these approaches and techniques for analysing and interpreting the human work, which eventually manifests in the design of technological products, systems and applications. recent years, researchers in psychology and economics have increasingly called for a consideration of more diverse subject populations.

Research goal

This PhD project is aimed at developing a framework for human-work interaction design that reflects recent advances in the pervasive condition of technology and its impact on the trans-mediated nature and experience of today’s workplace, which is now constituted and configured beyond physical boundaries. While any work domain can be used as a focus, we have a preference for work settings related to health, safety critical environments or software engineering teams.

Candidate profile

The ideal candidate should have an MSc or equivalent degree with a strong human-computer interaction component. Strong commitment to reaching research excellence and achieving assigned objectives is required, so as ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with workplace studies to explore the research goals and conclude with the validation of a proposed framework of interaction design for the workplace. 

Background knowledge and/or previous experience in the following areas (in order of preference), though not mandatory, will be considered very favourably: Human-Computer Interaction, Social Sciences: Psychology and/or Sociology

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Jose Abdelnour-Nocera: jose.abdelnour-nocera@uwl.ac.uk

Primary supervisor: Professor Jose Abdelnour-Nocera

Start dates: January, May and September of each academic year

Duration: This is a three-year position.

Research context

Research at the University of West London lives in an ecosystem of interdisciplinary research clusters. This PhD position is based in the School of Computing and Engineering. This particular proposal is focused on health digital communities.

This PhD position proposal is framed in the research field of mHealth and is in particular focused on the management of anticoagulation therapy. Coagulation disease is one of the most diffused health issues today and its management requires a continuous communication between patient and doctor. The introduction of mobile devices in anticoagulant therapy practice and management may help in improving the quality of life of patients and support remote doctor-patient communication.

In particular, we are interested in the application of such novel applications in resource-constrained environments, typical rural areas in which people do not have direct access to the internet, from home or from mobile phones. In such settings, the closest medical centre is located in the village and is aimed at serving all the people who leave in that area. Sometimes, the medical centres are provided with internet connection but its use is strongly restricted to doctors and not always fully available due to technical failures.

Research goal

The research activity will aim at investigating tools, techniques and methods that can be used by sociotechnical interaction designers and developers of mHealth applications in resource-constrained environments.

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science and combine solid theoretical background and excellent software development skills. Strong commitment to reaching research excellence and achieving assigned objectives is required, so as ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data and conclude with the validation of a proposed solution through a real-life user study. 

Background knowledge and/or previous experience in the following areas (in order of preference), though not mandatory, will be considered very favourably: Mobile (Web) design, Interaction design, Software internationalisation and localisation.

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

Questions regarding academic aspects of the project should be directed to Professor Jose Abdelnour-Nocera: jose.abdelnour-nocera@uwl.ac.uk

Primary supervisor: Professor Jose Abdelnour Nocera

Start dates: January, May and September of each academic year

Duration: This is a three-year position

Research context

This PhD will explore and discuss how Human Computer Interaction (HCI) as a field of knowledge and practice can contribute to develop platforms for digital democracy and participation.

These issues are mainly seen at two levels:

1. The optimal design of the digital environment of citizen participation platforms
2. Exploring how HCI can contribute to the development of new trends in political science such as e-democracy

The practice of designing digital platforms for citizen participation and democracy could benefit greatly from a multidisciplinary sociotechnical approach that incorporates into design reflection on issues of democratic theory and practice, legal and political science. Researchers have sought to articulate design patterns and evaluation tools for these platforms with general perspectives on the democracity of the processes they sustain. But citizen participation systems give rise to specific problems related to usability and user experience.

The user is both the institution, company, formal and in-formal collective, as well as the subjects that interact with these platforms. This workshop proposes a multidisciplinary exploration and discussion about design of digital platforms for citizen participation and democracy, including issues such as the necessary digital and technological resources, typology of tools that allow communication (to share knowledge), create community (to find and integrate individuals into a collective) and cooperation between individuals (to achieve common community goals), legality of the decisions taken in these platforms or subjective trust in their general function.

Research goal

This research aims to answer some of these questions:

• How can different approaches towards sociotechnical and interaction design promote or undermine democratic participation?
• What are the best design patterns and requirements that lead to effective platforms for digital democracy and participation?
• How can digital democracy platforms be evaluated?
• What are the best tools and methods to support communication, community formation and deliberation in democratic processes?
• Design thinking and innovation: how the design process should be led by democratic requirements and not by technology affordances?
• How to identify forms of democracy and associated practices through the presentation of relevant case studies
• How to identify and assess the nature and type of citizen participation in the process of design of these platforms

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Informatics, Computer Science, Design or Social Science, combining a solid theoretical background and excellent skills in any of these fields. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data and conclude with the validation of a proposed solution through real applications/case studies.

Background knowledge and/or previous experience in the following areas will be considered very advantageously:

• Human-Computer Interaction
• Participatory design
• Political and Social Science
• Sociotechnical design

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

For a more detailed description of the current PhD projects, please visit the Innovation and User Experience Research Group website.

Questions regarding academic aspects of the project should be directed to Professor Jose Abdelnour-Nocera: jose.abdelnour-nocera@uwl.ac.uk

Primary supervisor: Dr Ali Gheitasy

Start dates: January, May and September of each academic year

Duration: This is a three-year position

Research context

Artificial Intelligence (AI) has experienced rapid advancements in recent years, revolutionising various industries and aspects of human life. However, the increasing integration of AI systems raises concerns regarding user experience and ethical implications. Human-Centred Artificial Intelligence (HCAI) is an approach to designing and implementing AI that prioritises the well-being, needs and values of humans. It places humans at the core of AI systems, ensuring that technology is created and utilised in a manner that aligns with human values, enhances human capabilities and respects human rights and dignity. HCAI strives to develop systems that are not only technically advanced but also beneficial, responsible and aligned with societal values. Its goal is to create AI that serves human interests rather than replacing or disregarding them, fostering a positive and harmonious integration of AI into various aspects of human life.

Research goal

This research aims to contribute to the field of Human-Centred Artificial Intelligence by addressing challenges related to human behaviours and user experience in AI. It also seeks to investigate the ethical implications of AI systems, including issues of fairness, transparency, accountability, trust, privacy and bias. The research intends to develop ethical guidelines and frameworks for designing and deploying AI systems that uphold human values and rights. Additionally, it aims to contribute to the design and development of human-centred AI models, algorithms and interfaces in real-world scenarios by conducting user studies and evaluating the effectiveness, acceptance and impact of human-centred AI solutions on user experience and ethical considerations.

Candidate profile

The ideal candidate should have an MSc or equivalent degree in Computer Science and combine a solid theoretical background and excellent skills in Human Computing Interaction and/or Artificial Intelligence. Strong commitment to reaching research excellence and achieving assigned objectives is required, as well as an ability to work in a collaborative and interdisciplinary environment. It is expected that the PhD candidate will carry out applied research work that will start from the establishment of a theoretical framework, continue with the implementation of a software prototype and the experimentation with real data and conclude with the validation of a proposed solution through real applications/case studies.

Background knowledge and/or previous experience in the following areas/technologies, will be considered very advantageously:

• Human-computer interaction
• User experience design
• Artificial Intelligence
• Machine learning

All applicants for whom English is not their first language must also demonstrate their English language proficiency through evidence of IELTS at overall 7 (with 6.5 in all four skills) or by providing access to MA/MSc chapters or published work.

Further information

For a more detailed description of the current PhD projects, please visit the Intelligent Sensing and Vision Research Group website.

Questions regarding academic aspects of the project should be directed to Dr Ali Gheitasy: Ali.Gheitasy@uwl.ac.uk

• Nonlinear dynamics and chaos
• Applications of mathematics in electrical or civil engineering
• Stochastic differential equations and modelling
• Financial mathematics
• Educational mathematics

Contact Professor Anastasia Sofroniou or Dr Shihan Miah.

• Higher-dimensional gauge theory
• Special geometries with torsion
• Exceptional holonomy and torus symmetry

Contact Dr Thomas Madsen.

Primary supervisors: Professor Phil Cox, Dr Amalia Tsiami, Dr Fideline Tchuenbou-Magaia (University of Wolverhampton)

Start dates: January, May and September of each academic year.

Duration: 3 years full-time or 5 years part-time.

Research context

Foods are complex and dynamic. Even whilst sitting on the shelf, a variety of chemical, biochemical and microbiological agents are trying to change our food. Some can cause illness, the majority cause deterioration or off flavours, taints of change in texture. All of these activities can combine to make food unpalatable and so go to waste.

One such chemical process is rancidity in high fat foods; this is where metal ions, light and the food interact and produce a quite startling array of chemical compounds, some of which give off flavours and reduce palatability. Previous work has shown that some food proteins can be structured to allow them to adsorb metal ions and sequestrate them away from other parts of the food and so give products a longer shelf life and reduce wastage. We wish to explore this close and complicated interaction further.

This project will be in collaboration with the Chemical Engineering department at the University of Wolverhampton who have a strong expertise in food microstructure and function. The University of West London then provides experience and facilities for novel food production testing and sensory evaluation.

This project forms the first part of a growing collaboration between UWL and UoW in developing and testing new foods to help with a variety of modern conditions and experiences – dysphagia, obesity, sustainability being the most obvious. The supervisory team will consist of Professor Phil Cox and Dr Amalia Tsiami from the University of West London and Dr Fideline Tchuenbou-Magaia from the University of Wolverhampton. The project would suit a food scientist, a chemist, chemical engineer or biological sciences graduate with a research interest in food and eating activities.

Candidate profile

Entry requirements for our PhD course:

• First or Upper Second class (2:1) or equivalent in a relevant field
• MSc degree with Merit or above or have equivalent postgraduate or research experience
• International applicants only: an IELTS score (International English Language Testing System) of 6.5 or higher (with no element under 6.0) for international applicants. Applicants with a previous degree obtained in the UK are exempt from this requirement

Vice-Chancellor’s PhD Scholarships:

The University of West London is offering a number of opportunities for three year fully funded PhD Scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international students. Learn more

Primary supervisors: Professor Phil Cox, Dr Atiyeh Ardakanian, Professor Kristian Waters (McGill Canada)

Start dates: January, May and September of each academic year.

Duration: 3 years full-time or 5 years part-time.

Research context

Some types of waste waters are potentially very toxic to either the ecosystem or directly to human life. Contaminants come in a variety of forms: biological with a potential to drop the oxygen availability in watercourses, chemical incorporation of acid etc, pharmaceuticals or other xenobiotics and finally metals from mining, naturally occurring sources and manufacturing. Other wastewaters also contain metal ions with a significant value. This is where high value metals enter the water courses, generally through the action of humans, and are then lost. One such example is road dust where the rare earth metals blown out of car exhausts are collected and lost to the sewer system.

Recovery of precious metals from the sources above would be profitable if a low-cost, passive process could be found. This would require low-cost absorption media and a separation technology which again could run very passively and cheaply. Ideally, the absorption media should be ecologically benign, in case of spillage, and either cheap to regenerate or remanufacture between absorption campaigns.

A previous collaboration with McGill University, Canada, showed that structured waste food proteins could meet most of the objectives listed above. Acid mine drainage waters, containing excess amounts of iron and nickel were successfully treated and the absorbent removed using standard unit operations. We would like to extend this work to see if we could selectively remove precious or rare metal ions from complex feedstocks and concentrate up dilute streams to make the extraction profitable. Indeed some the target metal might also be toxic, for instance, lithium and removing these ion would also be a target for the work.

This project will look at fabricating new versions of the absorbent and methods to manipulate the extraction and recovery processes to give the technology a selective ability for precious metals. The supervisory team will consist of Professor Phil Cox and Dr Atiyeh Ardakanian from the University of West London and Professor Kristian Water from McGill Canada. The project would suit a chemist, chemical engineer or a civil engineer with a research interest in wastewater treatment.

Candidate profile

Entry requirements for our PhD course:

• First or Upper Second class (2:1) or equivalent in a relevant field
• MSc degree with Merit or above or have equivalent postgraduate or research experience
• International applicants only: an IELTS score (International English Language Testing System) of 6.5 or higher (with no element under 6.0) for international applicants. Applicants with a previous degree obtained in the UK are exempt from this requirement

Vice-Chancellor’s PhD Scholarships:

The University of West London is offering a number of opportunities for three year fully funded PhD Scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international students. Learn more

Primary supervisors: Professor Phil Cox, Dr Amalia Tsiami

Start dates: January, May and September of each academic year.

Duration: 3 years full-time or 5 years part-time.

Research context

Eating is a very complex process and involves multiple effects combining to give a bolus of food that is easily swallowed. Age and illness can make this process difficult or impossible and the normal diet gives way to thickened fluids and homogenised foodstuffs.

This project will look at various aspects of eating and the way in which humans prepare food for swallowing. The project will look at food formulation and its interaction with oral processing and bolus formation and swallowing. This will be a complex project and involve food science and engineering, consumer psychology to collect reliable data from subjects and also and complete understanding of the ethics surround food consumption in the laboratory to ensure subject safety, confidentiality and security.

The candidate will, along with supervisors, look for candidate food and food formulations that might give different eating responses from people with normal eating habits and those with different forms of dysphagia and try to elucidate the underlying mechanisms that either promote or prevent food ingestion and consumer satisfaction.

This project will form part of a larger piece of work alongside experts in mental health and an aging population and help provide underpinning data and mechanism to help derive strategies to help real world patients and people who struggle with their diet. The supervisory team will consist of Professor Phil Cox and Dr Amalia Tsiami from the University of West London. The project would suit a food scientist, food psychologist, chemist, chemical engineer or biological sciences graduate with a research interest in food and eating activities.

Candidate profile

Entry requirements for our PhD course:

• First or Upper Second class (2:1) or equivalent in a relevant field
• MSc degree with Merit or above or have equivalent postgraduate or research experience
• International applicants only: an IELTS score (International English Language Testing System) of 6.5 or higher (with no element under 6.0) for international applicants. Applicants with a previous degree obtained in the UK are exempt from this requirement

Vice-Chancellor’s PhD Scholarships:

The University of West London is offering a number of opportunities for three year fully funded PhD Scholarships (fees plus an annual stipend of £22,000 and £900 per student/per year to attend conferences). These will be available for all eligible UK and international students. Learn more

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