Keywords: Predictive control, machine learning, data-driven models, model-based reinforcement learning, optimization, autonomous systems
This position focuses on fundamental research at the intersection of systems & control, machine learning, and optimization. The project aims to develop new algorithms that leverage experimental data to control complex systems. The focus is on developing new methods that have strong theoretical properties. These algorithms are expected to support control in emerging engineering applications and ensure efficient and safe operation.
Students will jointly develop new algorithms and a corresponding theoretical framework that ensures desired properties (stability, performance, safety). Students will apply their methods on relevant systems, using simulations or experimental setups as appropriate. Students will gain hands-on experience in algorithm design, theoretical analysis of control systems, and implementation, developing expert knowledge in data-driven modelling, optimization, and safe control design. The PhD position is supervised by Dr. Johannes Köhler in the Department of Mechanical Engineering at Imperial College London. The PhD is fully funded for up to 3.5 years, providing an excellent environment for research, collaboration, and academic growth within a leading international institution.
Requirements:
• Master’s (MSc or MEng), in Mechanical Engineering, Control, Applied Mathematics, Computer Science, or a related discipline.
• Strong expertise in at least one of the following is required:
a) data-driven models (machine learning, system identification, or statistical methods),
b) advanced control (predictive control, robust control, or model-based RL).
• Strong commitment to fundamental, curiosity-driven research
• Excellent analytical reasoning, creative problem-solving abilities
• Capacity to work independently and to collaborate on interdisciplinary research
• Strong communication skills
• Proficiency in MATLAB or Python.
• An interest in mechanical engineering, medical technologies, or robotic systems is advantageous.
How to apply: If you are interested, please submit a CV, motivation letter, academic transcripts, and the contact details of at least one referee as PDFs to j.kohler@imperial.ac.uk. Applications will be reviewed on a rolling basis; ideally, applications should be submitted by 31 January 2026. Imperial College London is an equal opportunity employer and encourages applications from candidates of all backgrounds.
More information: https://www.imperial.ac.uk/mechanical-engineering/study/phd/phd-opportunities/current-phd-studentships/
-> PhD Studentship in Data-Driven and Predictive Control (Johannes Köhler)
