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Research Studentship in Control Engineering and Energy Systems

Research Studentship in Control Engineering and Energy Systems

3.5-year D.Phil. studentship 

Project: Data-driven control and optimisation for energy systems

Supervisors: Jack Umenberger

This project will focus on solving problems in data-driven control and optimization, motivated by challenges arising in the operation of electrical power systems with high penetration of variable renewable energy sources, such as wind and solar. The approaches taken will depend on the candidate’s skills and interests, but will generally sit at the intersection of control, optimisation, and machine learning. Specific opportunities may include: building machine learning based tools to accelerate the solution of optimisation problems encountered in real-time operation of the grid (e.g. security constrained economic dispatch); developing theory and algorithms for decision-making under uncertainty, especially uncertainty arising from forecasts (e.g. wind, solar, demand); optimisation and control of grid-scale storage assets, such battery energy storage systems (BESS).

The candidate will be part of both the Control Group and Oxford University’s ZERO Institute. Consequently, they will be supported by a multidisciplinary team of academic experts, with strong connections to industry, and will be able to collaborate widely with other researchers working in control, optimisation, and energy systems. The project will enable the candidate to build a broad skillset in machine learning, optimisation, and control of energy systems, providing a great foundation for future careers in academia and/or industry.


Both domestic and international students are eligible to apply.

Award Value

100% of course fees will be covered. The stipend (tax-free maintenance grant) is c. £18,622 p.a. for the first year, and at least this amount for a further two and a half years. 

Candidate Requirements

Prospective candidates will be judged according to how well they meet the following criteria:

  • A first class (or strong 2:1) degree in any of Engineering, Materials Science, Computer Science, Physics, Mathematics.
  • Excellent English written and spoken communication skills.

It is desirable that candidates possess expertise in some (but not all, or even most) of the following areas:

  • Applied mathematics, control engineering, and numerical optimisation
  • Machine learning and/or data science
  • Computer programming and/or software engineering
  • Electrical engineering and/or power systems.

Application Procedure

Informal enquiries are encouraged and should be addressed to Dr Jack Umenberger (

Candidates must submit a graduate application form and are expected to meet the graduate admissions criteria.  Details are available on the course page of the University website.

Please quote 24ENGCO_JU in all correspondence and in your graduate application.

Application deadline: noon on 1 December 2023 (In line with the December admissions deadline, set by the University)

Start date: October 2024