Post-Doctoral Research Associate in AI-Driven Process Modelling and Control

We are looking for a Research Associate (post-doctoral) to conduct research in AI-enabled process modelling and optimisation, as part of a new collaboration between Repsol S. A. and Imperial College London. We will study two interlinked objectives: (1) the automated generation of dynamic process models using large language models (LLMs), and (2) the data-driven discovery of control strategies trained on those models, focusing on the sim-to-real transfer gap. The work will be validated using real-world industrial case studies and is designed to produce both publishable scientific contributions and deployable engineering/software tools.

The successful candidate will collaborate with Dr Calvin Tsay in the Department of Computing, along with Dr Antonio del Río Chanona and Dr Mehmet Mercangöz in the Department of Chemical Engineering, and with relevant process modelling teams at Repsol S.A., who will provide access to real-world operating data and case studies. Furthermore, they will be a member of the broader Sargent Centre for Process Systems Engineering at Imperial College London. Further information on the research of Drs del Río Chanona, Mercangöz, and Tsay, as well as the Sargent Centre can be found at https://www.optimlpse.co.uk, https://profiles.imperial.ac.uk/m.mercangoz, https://www.doc.ic.ac.uk/~ctsay/, and https://www.imperial.ac.uk/process-systems-engineering/.

Alongside research and publication, the Research Associate will be expected to contribute to developing, documenting, and maintaining software tools arising from the project, with the aim of releasing stable AI-enabled workflows. This role may include:

Building frameworks to couple AI models with process simulators.

• Designing learned representations for dynamic process systems.
• Studying data-driven and hybrid control systems.
• Conducting independent and collaborative research within the group.
• Publishing results in leading venues across applied AI, process control, and process systems engineering.
• Contributing to research projects involving optimisation and machine learning.

Candidates should have, or expect shortly to obtain, a PhD in Computer Science, Chemical Engineering, Operations Research, or a closely related discipline or hold equivalent research/industrial experience. You will have practical experience within a research environment and publications in top conferences or journals in the field and experience in the following areas:

• dealing with industry partners and research collaborators
• applied AI, process systems engineering, mathematical optimisation, and/or applied mathematics
• a broad range of techniques for data-driven control, including data-driven MPC, system identification, and/or reinforcement learning is desirable

Please see job description for a full list of requirements.