Postdoctoral Research Associate in Machine Learning for Grid-Edge Flexibility

University of Oxford
Oxford
2 weeks ago
Applications closed

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We are seeking

a full-time Postdoctoral Research Associate in Machine Learning for Grid-Edge Flexibility to join the Power Systems Architecture Lab within the Department of Engineering Science at the University of Oxford. The position is funded by the EPSRC-FNR project “FleXEdge - Data-Driven Cloud-to-Edge Computing for Scalable Near Real-Time Local Flexibility Markets”. It is fixed-term for 36 months (or up to the end of June 2028). You will undertake original research on multi-agent reinforcement learning to coordinate grid-edge flexibility across spatial and temporal scales, while accounting for network constraints and uncertainty. Collaborating with other researchers on the project, you will also contribute to the design of a supporting cloud-to-edge computing architecture and profit-sharing mechanisms for grid-edge device federations participating in local and national flexibility markets. This will bring together research on power systems modelling, multi-agent control, machine learning and mechanism design. The project will involve close collaboration with project teams at Imperial College London, the Edinburgh Parallel Computing Centre (EPCC) and the Luxembourg Institute of Science and Technology (LIST). You will also have the opportunity to work with industry partners: the National Energy System Operator (NESO), SP Energy Networks, Piclo, Siemens, Energy Systems Catapult, and Typhoon HIL. You should hold a relevant PhD/DPhil, or be near completion, in electrical engineering, computer science, applied mathematics or another related area. (For candidates with an undergraduate/Masters degrees, there is the potential for appointment at Grade 6 (£34,982 p.a. - £40,855 p.a.). For more information about working at the Department, see

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