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

This 3.5-year D.Phil. studentship at the University of Oxford's Department of Engineering Science focuses on modernizing control engineering using data-driven algorithms, optimization, and statistical learning. Supervised by Professor Antonis Papachristodoulou and Professor Kostas Margellos, the project investigates how learning tools and world models can enhance algorithm scalability while providing rigorous robustness guarantees. The appointed candidate will engage in interdisciplinary research intersecting control theory and artificial intelligence, collaborating with academic and industrial partners. The position offers an ideal foundation for future careers in academia, biotechnology, or deep-tech entrepreneurship.

• Conduct independent and collaborative research at the intersection of control theory, optimization, and machine learning.
• Develop novel algorithms to improve the scalability, efficiency, and reliability of large-scale uncertain systems.
• Investigate the integration of world models with traditional control frameworks to provide mathematical robustness guarantees.
• Engage with the research activities of the MSc in AI for Engineering Biology and collaborate with multidisciplinary industrial and academic partners.
• Disseminate research findings through academic publications, presentations, and technical reports.