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Students
Tuition Fee
USD 24,949
Per year
Start Date
Medium of studying
On campus
Duration
73 months
Program Facts
Program Details
Degree
PhD
Major
Electrical Engineering | Power Engineering | Power Systems Technology
Area of study
Engineering
Education type
On campus
Timing
Full time
Course Language
English
Tuition Fee
Average International Tuition Fee
USD 24,949
Intakes
Program start dateApplication deadline
2023-04-24-
2023-09-19-
2024-01-09-
About Program

Program Overview


Research profile

Our research focuses on the development of algorithms to improve distribution efficiency, and strategies to allow mass power generation from renewable energy sources to be efficiently integrated with the grid. Other areas of expertise include power electronics for efficient power conversion. Challenges for the future include efficient conversion of thermal to electrical energy, Direct Current (DC) power networks in commercial buildings and homes, and innovations to reduce electrical energy demand and optimised demand control.

Find out about the exciting research we do: 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

  • Brunel Interdisciplinary Power Systems
  • Wireless Network and Communication
  • Intelligent Digital Economy and Society
  • Sensors and Instrumentation
  • Institute of Digital Futures
  • Digital Manufacturing
  • Media Communication
  • AI Social and Digital Innovation
  • Electronic Systems
  • You can explore our campus and facilities for yourself by taking our virtual tour.

    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.


    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:

  • 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
  • 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
  • Development of personalised services and applications for healthcare, supervised by Fotios Spyridonis, George Ghinea, Zidong Wang and Xiaohui Liu
  • Digital Stone: Robotic Construction of a Masonry Arch Bridge, supervised by Michael Rustell and Tatiana Kalganova
  • Distributed energy resources optimisation, supervised by Maysam Abbod
  • 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
  • Machine learning for natural language modelling and processing, supervised by Nikolaos Boulgouris
  • 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
  • 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
  • Using deep learning for weed detection, supervised by Tatiana Kalganova
  • SHOW MORE
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