Publisher
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Queen Mary University of London

PhD in Intelligent Optimisation and Control for Future Power Systems Queen Mary University of London in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Funded PhD project. The student will receive home-rate tuition fees and a London stipend at QMUL stipend rates, currently £22,618 per year in 2026/27, for up to 3 years. Non-home/non-UK students may apply but must cover the substantial tuition fee difference themselves.

Deadline

Nov 30, 2026

Country flag

Country

United Kingdom

University

Queen Mary University of London

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Keywords

Computer Science
Environmental Science
Electrical Engineering
Smart Grid Technology
Mathematics
Statistics
Power System
Control System
Distributed Energy
ML

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About this position

Queen Mary University of London is advertising a funded PhD project in Intelligent Optimisation and Control for Future Power Systems within the School of Electronic Engineering and Computer Science in London, United Kingdom.

The project focuses on future power systems that integrate renewable generation, electric vehicles, energy storage, heat pumps, data centres, and other flexible resources. Research will explore network-constrained optimisation, privacy-preserving energy flexibility management, and machine learning-based grid control under adverse conditions. The aim is to develop novel optimisation and ML-based frameworks for coordinating heterogeneous resources while maintaining security, resilience, and economic performance.

Applicants are encouraged from Electrical Power Engineering or Electrical and Electronic Engineering backgrounds, and strong candidates from Computer Science or Artificial Intelligence with an interest in power systems are also welcome. Required skills include strong programming ability (e.g. Python) and knowledge of machine learning. Research experience or publications are considered an advantage.

Funding includes home-rate tuition fees and a London stipend at QMUL rates, currently £22,618 per year for 2026/27, for up to 3 years. Non-home/non-UK students may apply, but they must cover the tuition fee difference themselves because no additional funding or fee waiver is available for that gap.

Supervisor: Dr Da Huo ([email protected]). Additional enquiries can be sent to Mrs Melissa Yeo (administrative) or Dr Arkaitz Zubiaga (academic).

Application deadline: 30 November 2026. Interviews will be held in December, and the successful candidate is expected to start in January, April, or September 2027.

How to apply: follow Queen Mary’s PhD application instructions and submit a CV, cover letter, research proposal (max 500 words), two references, and English language/certification documents if applicable.

Funding details

Funded PhD project. The student will receive home-rate tuition fees and a London stipend at QMUL stipend rates, currently £22,618 per year in 2026/27, for up to 3 years. Non-home/non-UK students may apply but must cover the substantial tuition fee difference themselves.

What's required

Applicants should have a background in Electrical Power Engineering or Electrical and Electronic Engineering; candidates from Computer Science or Artificial Intelligence with a strong interest in power systems research are also welcome. Strong programming skills, especially Python, and knowledge of machine learning are required. Demonstrated research interest such as publications will be an advantage. Non-home/non-UK students may apply but will be responsible for the tuition fee difference because no additional funding or fee waiver is available to cover the gap.

How to apply

Submit the application through Queen Mary University of London’s PhD application instructions page. Include a CV, cover letter stating UK scholarship eligibility, a 500-word research proposal, two references, English language certificate if needed, and other certificates. Contact the supervisor or listed administrative/academic contacts for enquiries.

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