Program Overview
Research profile
The Electronic and Electrical Engineering degrees offer a wide range of research disciplines, related to Electronic, Wireless Communications, Satellite Communication, Measurement and Instrumentation, Control Systems, Robotics, Image and Signal Processing, Artificial Intelligence Applications such as Modelling, Features Extraction, Classification, Optimisation, Scheduling, Navigation and other applications, Information Engineering, and Computer Networks. The research is conducted in a highly stimulating academic environment which can be categorised within the research groups, namely Electronics Systems Research, Sensors and Measurement, Power Systems, Wireless and Computer Communication. The Department enjoys an international reputation for its work and prides itself in allowing students the freedom to realise their maximum potential.
Find out about the exciting research we do in this area. Browse profiles of our experts, discover the research groups and their inspirational research activities you too could be part of. We’ve also made available extensive reading materials published by our academics and PhD students.
Learn more about research in this area.
Browse the work of subject-relevant research groups
AI Social and Digital Innovation
Computer Science for Social Good
Electronic Systems
Brunel Software Engineering Lab
Institute of Digital Futures
Computational Biology
Interactive Multimedia Systems
Brunel Interdisciplinary Power Systems
Sensors and Instrumentation
Human Computer Interaction
Intelligent Data Analysis
Digital Economy
Media Communication
Digital Manufacturing
Modelling and Simulation
Intelligent Digital Economy and Society
Smart Technology Advancements in Health and Rehabilitation
Wireless Network and Communication
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Program Outline
Research journey
This course can be studied 3 years full-time or 6 years part-time, starting in January. Or this course can be studied 3 years full-time or 6 years part-time, starting in October. Or this course can be studied 3 years full-time or 6 years part-time, starting in April.
Find out about what progress might look like at each stage of study here: Research degree progress structure.
Careers and your future
You will receive tailored careers support during your PhD and for up to three years after you complete your research at Brunel. We encourage you to actively engage in career planning and managing your personal development right from the start of your research, even (or perhaps especially) if you don't yet have a career path in mind. Our careers provision includes online information and advice, one-to-one consultations and a range of events and workshops. The Professional Development Centre runs a varied programme of careers events throughout the academic year. These include industry insight sessions, recruitment fairs, employer pop-ups and skills workshops.
In addition, where available, you may be able to undertake some paid work as we recognise that teaching and learning support duties represent an important professional and career development opportunity.
Find out more.
Find a supervisor
Our researchers create knowledge and advance understanding, and equip versatile doctoral researchers with the confidence to apply what they have learnt for the benefit of society. Find out more about working with the Supervisory Team.
You are welcome to approach your potential supervisor directly to discuss your research interests. Search for expert supervisors for your chosen field of research.
While we welcome applications from students with a clear direction for their research, you can also choose one of our selected project topics:
PhD topics
While we welcome applications from student with a clear direction for their research, we are providing you with some ideas for your chosen field of research:
A Machine Learning Approach for Migrating to Microservices, supervised by
Nour Ali
Additive manufacturing and sustainability, supervised by
Eujin Pei
Advanced planar MIMO sparse imaging for target detection, supervised by
Hongying Meng and Shaoqing Hu
AI for power management of a domestic district integrated energy system, supervised by
Maysam Abbod
AI system for Vehicle to Grid power management, supervised by
Maysam Abbod
Automatic computational fluid-dynamics, supervised by
James Tyacke
Autonomous robots for non-disruptive inspection of utility and sewage systems, supervised by
Md Nazmul Huda
Brain wave analysis and modelling with graph signal processing, supervised by
Nikolaos Boulgouris
Building Information Model Development Using Generative Adversarial Networks, supervised by
Michael Rustell and Tatiana Kalganova
Can AI based robot car win the race, supervised by
Dong Zhang
CFD modelling of plasma flow control, supervised by
James Tyacke
Circular economy in electricity networks with high penetration of renewable energy and flexibility provisions from end-of-life EV storage, supervised by
Ioana Pisica
Cultivating virtual embodiment using non-invasive brain stimulation, supervised by
Monica Pereira and Nadine Aburumman
Deep Learning for Medical Imaging, supervised by
Yongmin Li
Deep learning-based autonomous diagnosis of gastrointestinal tract cancers, supervised by
Md Nazmul Huda
Design and Development of a Capsule Robot for Medical Applications, supervised by
Md Nazmul Huda
Design, development, and optimisation of a six-legged robot for hybrid walking and manipulation in challenging environments, supervised by
Mingfeng Wang
Developing a device for marine life and water quality monitoring, supervised by
Gera Troisi
Developing computational models to understand the evolution of bidirectional catalysts in biology, supervised by
Sarath Dantu
Development of personalised services and applications for healthcare, supervised by
Fotios Spyridonis, George Ghinea, Zidong Wang and Xiaohui Liu
Development of resilient hospitals through enhanced built environment design and research, supervised by
Kangkang Tang
Digital Stone: Robotic Construction of a Masonry Arch Bridge, supervised by
Michael Rustell and Tatiana Kalganova
Disruptive Digital Experiences, supervised by
Harry Agius and Damon Daylamani-Zad
Distributed energy resources optimisation, supervised by
Maysam Abbod
Energy and CO2 Awareness during Software Design and Development, supervised by
Nour Ali
Explaining model decisions through dialogue, supervised by
Isabel Sassoon
Exploring the potential of serious games to enhance user engagement with real-world applications, supervised by
Fotios Spyridonis and Damon Daylamani-Zad
Fast implementation of Deep Neural Networks for IoT devices, supervised by
Lu Gan
Intelligent, Interpretable and Adaptive Design of Steel Structures using Deep Learning and NLP, supervised by
Michael Rustell and Tatiana Kalganova
IoT techniques for disaster prediction and prevention, supervised by
Take Itagaki
Large Language Models (LLM) for Automated Finite Element Analysis, supervised by
Michael Rustell and Tatiana Kalganova
Machine learning approaches in health data science for risk prediction of cardiovascular diseases, supervised by
Raha Pazoki
Machine learning for natural language modelling and processing, supervised by
Nikolaos Boulgouris
Machine learning for sustainable transportation systems, supervised by
Muhammad Shafique
Medical image segmentation and classification, supervised by
Maysam Abbod
Metasurfaces for smart environments, supervised by
Nila Nilavalan
MIMO antenna array for 5G handset applications, supervised by
Nila Nilavalan and Shaoqing Hu
Natural Language Processing for Business Intelligence, supervised by
Yongmin Li
Real-time Visual and Haptic Feedback of Grasping Movements in Virtual Reality, supervised by
Nadine Aburumman
Study of stray current induced corrosion in railway construction, supervised by
Kangkang Tang
Swarm of multiple co-operative and autonomous low-cost robots for search and rescue, supervised by
Md Nazmul Huda
Use of Large Language Models (LLM) as a Structural Engineering Design Assistant, supervised by
Michael Rustell and Tatiana Kalganova
User experience in Extended Reality environments and applications, supervised by
Fotios Spyridonis and Damon Daylamani-Zad
Using deep learning for weed detection, supervised by
Tatiana Kalganova
Using Machine Learning to Simulate Macroscopic phenomena for Fluid Dynamics, supervised by
Nadine Aburumman
