PhD Studentship: Data-driven Control Engineering for Large-scale Uncertain SystemsUniversity

Research Studentship in Control Theory, Optimization and Learning

3.5-year D.Phil. studentship

Project: Data-driven control engineering for large-scale uncertain systems.

Supervisors: Prof Antonis Papachristodoulou and Prof Kostas Margellos

This project will focus on solving fundamental challenges at the modernization of Control Engineering using tools from data-driven algorithms, advancements on optimization and statistical learning. Cross-fertilizing ideas across these domains can be of mutual benefit for all areas. This project will focus on how the use of learning tools can be used to improve control engineering tools. This could look at the problem of algorithm scalability and efficiency, or at how concepts such as “world models” can be used to extend our thinking about traditional modelling frameworks in control. At the same time, this project will investigate how control theory can offer robustness guarantees when employed in conjunction with such modelling concepts.

The approaches taken will depend on the candidate’s skills and interests, but may include developing control theoretic and AI methods that advance our ability to design reliable and robust control systems. Potential project applications will involve applying these skills to large scale uncertain systems and integrate them with “world models”. The project will enable candidates to build a broad skillset in theory and algorithm design, engineering, and the application of control to new AI technologies, providing an ideal foundation for future careers in academia, the bioscience industry, or entrepreneurship/start-ups.

The studentship will integrate with research activities carried out within the new MSc on AI for Engineering Biology, hosted within the Department of Engineering Science, University of Oxford. As such the candidate will be exposed to research by a multidisciplinary team of academic and industrial experts with experience in related areas of biotechnology and have opportunities to collaborate with industrial and academic partners nationally and internationally.

Eligibility

This studentship is open to UK students (full award – home fees plus stipend).

There may be flexibility to support international students. If you are an international student and want to apply for this studentship please contact the supervisor to see whether the flexibility might be available for you.

Award Value

Course fees are covered at the level set for UK students (at least £10940 p.a.). The stipend (tax-free maintenance grant) is at least c. £21805 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, Computer Science, Physics, or 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:

Control Engineering, Computer Science, Artificial Intelligence, Applied Mathematics, or Computer Science

Electrical/Mechanical Engineering and Robotics

Programming / Software Engineering

Application Procedure

Informal enquiries are encouraged and should be addressed to Prof Antonis Papachristodoulou and Prof Kostas Margellos ([email protected], [email protected]).

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

Please quote 27ENGCO_AP in all correspondence and in your graduate application.

Application deadline: noon on 2nd December 2026 (In line with the December admissions deadline, set by the University)

Start date: October 2027