Postdoctoral Researcher in Physics-Grounded Agentic AI for Power-System Resilience Across Timescales

Postdoctoral Researcher in Physics-Grounded Agentic AI for Power-System Resilience Across Timescales
EPFL - Swiss Federal Institute of Technology Lausanne
Lausanne, Switzerland

PROJECT DESCRIPTION

The objective of this project is to develop AI methods that strengthen power-system resilience across long-term infrastructure planning, operational decision-making, and disruption response. The research will investigate how infrastructure investments and adaptive operating strategies can jointly sustain critical services under uncertain disturbances, changing operating conditions, and component or communication failures.
The project will combine agentic AI with physics-informed learning, graph neural networks (GNNs), and multi-agent reinforcement learning (MARL). Physics-informed graph models will represent network interactions and support the assessment of grid behaviour across changing topologies and operating conditions. MARL will enable the coordination of distributed resources, including storage, generation, and flexible demand, under partial observability and communication constraints. Agentic AI systems will support structured reasoning by formulating and investigating disruption scenarios, coordinating simulation and optimization tools, and evaluating proposed interventions.
A particular emphasis lies on connecting planning and operation: understanding how adaptive operational capabilities influence the value of investments in network reinforcement, hardening, storage, and flexibility, and how planning decisions determine the options available during disruptions. Trustworthiness will be central, including uncertainty quantification, physical verification of proposed actions, transparent decision support, and robustness to previously unseen conditions.
The precise research focus will be developed jointly with the successful candidate, building on their expertise and the lab’s research strengths.
CANDIDATE PROFILE
We are looking for a candidate with a strong analytical background and a PhD, completed or nearing completion, in Electrical Engineering, Computer Science, Control, Applied Mathematics, Operations Research, or a related field. You should have a solid foundation in machine learning or optimization, strong programming skills, and a demonstrated ability to conduct rigorous, independent research.
Strong expertise in machine learning is required, with demonstrated research experience in one or more of the following areas:
Agentic AI, foundation models, or model-based reasoning.
Graph neural networks, physics-informed learning, or hybrid modelling.
Multi-agent reinforcement learning, constrained learning, or learning-based decision-making under uncertainty.
Experience in power-system modelling, planning, operation, or resilience is a strong asset. Candidates with a strong ML background and a clear motivation to develop methods for power-system applications are encouraged to apply.
We expect the candidate to be self-driven, collaborative, and creative, with strong problem-solving abilities and out-of-the-box thinking. Professional command of English, both written and spoken, is mandatory.
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EPFL - Swiss Federal Institute of Technology Lausanne