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Antonis Papachristodoulou

Top university

4 days ago

PhD Studentship in Data-driven Control Engineering for Large-scale Uncertain Systems University of Oxford in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Dec 2, 2026

Country flag

Country

United Kingdom

University

University of Oxford

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Keywords

Computer Science
Biomedical Engineering
Mechanical Engineering
Electrical Engineering
Mathematics
Artificial Intelligence
Robust Control
Software Engineering
Optimisation
Robotics
Applied Mathematic
bio engineering

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About this position

[Full award for UK students: home fees plus stipend. Course fees covered at the UK rate (at least £10,940 per year). Stipend is at least about £21,805 per year for the first year and for a further two and a half years.]

PhD/D.Phil. studentship at the University of Oxford in Control Theory, Optimization and Learning, focused on data-driven control engineering for large-scale uncertain systems.

This project sits at the intersection of control engineering, optimization, statistical learning, and AI. The research aims to address core challenges in modern control design, including scalability, efficiency, robustness guarantees, and the use of emerging “world models” to extend traditional modelling frameworks. Potential work may involve developing control-theoretic and AI methods for reliable, robust control systems and applying them to large-scale uncertain systems.

The studentship integrates with research activities connected to the new MSc in AI for Engineering Biology in Oxford’s Department of Engineering Science. Successful candidates will join a multidisciplinary environment and may collaborate with academic and industrial partners nationally and internationally. The project offers training in theory, algorithm design, engineering, and the application of control methods to AI technologies, with career pathways in academia, bioscience industry, and entrepreneurship/start-ups.

Funding: full award for UK students, including home fees and a stipend. Course fees are covered at the UK rate (at least £10,940 per year). The stipend is at least approximately £21,805 per year for the first year and continues at that level for a further two and a half years. There may be flexibility to support international students, but applicants from outside the UK should contact the supervisors before applying.

Eligibility: applicants should have a first-class or strong 2:1 degree in Engineering, Computer Science, Physics, or Mathematics, and strong English communication skills. Relevant experience in control engineering, AI, mathematics, robotics, or software/programming is desirable.

Deadline: noon on 2 December 2026. Start date: October 2027.

Apply via: the University of Oxford graduate application system using the Apply button on the posting page. Quote reference 27ENGCO_AP in all correspondence and in the application.

Funding details

Available

What's required

Applicants should have a first class or strong 2:1 degree in Engineering, Computer Science, Physics, or Mathematics. Excellent written and spoken English is required. Desirable background includes some experience in Control Engineering, Computer Science, Artificial Intelligence, Applied Mathematics, Electrical or Mechanical Engineering, Robotics, and Programming/Software Engineering. International students may be considered if supervisor flexibility is available; candidates should contact the supervisors to confirm eligibility.

How to apply

Submit a graduate application form via the Apply button on the University of Oxford course page. Quote 27ENGCO_AP in all correspondence and in your application. Informal enquiries can be sent to Antonis Papachristodoulou and Kostas Margellos by email.

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Professors

Prof at University of Oxford

University Name
Country flag

United Kingdom

Research Interests

Control System

Artificial Intelligence

Synthetic Biology

Optimal Control

Biomedical Engineering

Prof at University of Oxford

University Name
Country flag

United Kingdom

Research Interests

Behavioral Economics

Microeconomics

Robust Control

Distributed Algorithms

Multi-agent System