Title: SCH43: Towards an intelligent hybrid energy and transportation system in the presence of large-scale EVs and renewable energy
Funding: A full Home/EU fee waiver or equivalent fee discount for overseas students (£5,103 in 2020-21) (further fee details - international students will need to pay the balance of their fees) plus a doctoral stipend equivalent to the RCUK Minimum Doctoral Stipend (£15,285 in 2020-21).
Application deadline: Tuesday 31 March 2020
Start date: October 2020
Duration: 3 years (full time)
Location: Colchester Campus
Based in: Department of Mathematical Sciences (in collaboration with School of Computer Science and Electronic Engineering)
Increasing efforts have been made in large-scale integration of electric vehicles and renewable energy to reduce the greenhouse emissions in transport and energy sectors in order to achieve a net zero emissions society for the UK.
However, the large-scale uptake of EVs and renewable energy brings new constraints, challenges and mutual effects to operations of existing energy and transportation systems and will create a coupled hybrid system exhibiting complex interactive behaviours among different parties with a series of challenging and open research problems to be answered.
This project aims to develop an intelligent hybrid energy and transportation system in the presence of large-scale EVs and renewable energy with the-state-of-the-art game-theoretic and agent-based decision making, stochastic optimisation and deep learning methods.
The successful candidate will receive supervisions from experts in both Department of Mathematical Sciences and School of Computer Science and Electronic Engineering.
During the project, the candidate will have the opportunity to visit external research organisations and build contacts with industry.
The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,285 in 2020-21), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.
Lead supervisorDepartment of Mathematical Sciences, University of Essex
Fanlin Meng is a Lecturer in Data Science. He obtained his BSc from China University of Mining and Technology (2008), MSc from Xiamen University (2011) and PhD from University of Manchester (2015). His primary research interests include Machine Learning, Game theory and Optimisation including their applications to smart grids and energy markets, intelligent transportation systems modelling, and the sustainability.
Co-supervisorSchool of Computer Science and Electronic Engineering, University of Essex
Dongbing Gu is a professor in School of Computer Science and Electronic Engineering, University of Essex. His current research interests include autonomous systems, robotics, navigation and control, mapping and localisation, cooperative control, and machine learning. He has published more than 200 papers in international conferences and journals. His research has been supported by Royal Society, EPSRC, EU FP7, British Council, and industries. He serves as a board member for some international journals.
Co-supervisorDepartment of Mathematical Sciences, University of Essex
Xinan Yang is a Senior Lecturer in Operational Research. She obtained her first degree in Applied Mathematics from Fudan University (Shanghai, China) and MSc/PhD in Operational Research from University of Edinburgh. Her research interests lie in stochastic optimisation, (approximate) dynamic programming, reinforcement learning.
BSc (1st) in Data Science, Computer Science, Maths or other relevant subjects with significant maths and computer science components.
An MSc in relevant subjects is an advantage but not compulsory.
You can apply for this postgraduate research opportunity online.
Please include your CV, covering letter, personal statement, and transcripts of UG and Masters degrees in your application.
The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.
When you apply online you will be prompted to fill out several boxes in the form:
If you have any informal queries about this opportunity please email the lead supervisor, Dr Fanlin Meng (firstname.lastname@example.org).
You can find the terms and conditions of this studentship here (PDF).