Research Assistant/Associate in Data-driven Control and Optimisation for Energy Systems

Location: South Kensington, London (hybrid working possible by agreement)About the roleYou will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UKPowerNetworks, SSEEnergySolutions, and the University of East London, you will develop robust economic Model Predictive Control (MPC) algorithms, innovative coalition-formation techniques, and validate these through high-fidelity simulations.What you would be doingYou will design, implement and validate innovative data-driven economic model predictive control (MPC) methods to enable large groups of buildings to dynamically form coalitions and provide flexible energy services. Your work will incorporate advanced robust MPC techniques, including scenario-based and tube-based approaches, to ensure reliable operation despite significant uncertainty in weather, demand and energy prices.In collaboration with UK Power Networks and SSE Energy Solutions, you will apply your methods in detailed simulation studies using Imperial’s high-performance computing facilities, assessing performance in realistic scenarios. You will regularly present your findings to academic and industrial audiences, publish in leading journals, and actively contribute to the development and dissemination of open-source software. Additionally, you will support junior researchers and postgraduate students in your team.Working closely with collaborators at the University of East London, you will integrate robust MPC algorithms with advanced coalitional control strategies, determining optimal ways for groups of buildings to share resources and benefits. You will investigate and quantify trade-offs between individual objectives and collective outcomes, focusing on scalability, economic viability, and robustness to realistic operational uncertainty.What we are looking forPhD (or equivalent) in control engineering or closely related discipline.Track record in at least two areas: model predictive control, robust/distributed control, data-driven identification/control, numerical optimisation.Strong programming skills in at least two of the following: Julia, MATLAB, C/C++, Python.Demonstrated ability to produce high-quality journal publications and scientific manuscripts.Experience presenting research clearly at international conferences and industry workshops.Proven ability to manage your time effectively and prioritise tasks to meet research deadlines.Experience working collaboratively within multidisciplinary teams or industrial partnerships.Enthusiasm for mentoring junior researchers and contributing positively to a collaborative research environment.Desirable: experience with building energy or power system applications, cooperative or coalitional game theory, or high-performance computing workflows.What we can offer youYou will join a vibrant research community based at our central London campus in South Kensington, providing easy access to cultural institutions, excellent transport links, and a stimulating research environment.Opportunity to shape next generation control theory within a world leading research groupYou can find further details of the benefits we offer by reading the full job advert on Imperial College London jobsThe post is available from 1st March 2026 for up to 24 months, based in the Department of Electrical and Electronic Engineering at Imperial College London (South Kensington).To apply, please click the \’Apply\’ button, above.Please ensure you include a completed application form with your submission.Closing date: Midnight on Sunday, 2nd November 2025£43,863 to £57,472 per annum #J-18808-Ljbffr