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Postdoctoral Researcher in Physics-Grounded Agentic AI for Power-System Resilience Across Timescales EPFL in Switzerland
Degree Level
Postdoc
Field of study
Computer Science
Funding
Postdoctoral position at 100% full-time, fixed-term (CDD), duration 1 year renewable; EPFL states competitive salaries.
Country
Switzerland
University
EPFL
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About this position
This postdoctoral project at EPFL is hosted by the Intelligent Maintenance and Operations Systems (IMOS) Lab in Lausanne. It focuses on developing new AI methods for power-system resilience across multiple timescales, spanning long-term infrastructure planning, operational decision-making, and disruption response.
The research combines agentic AI with physics-informed learning, graph neural networks, and multi-agent reinforcement learning to model and support resilient grid operation. A key challenge is to represent network interactions across changing topologies and operating conditions, while coordinating distributed resources such as storage, generation, and flexible demand under partial observability and communication constraints.
The project also emphasizes the link between planning and operation: understanding how investment decisions in reinforcement, hardening, storage, and flexibility shape what is possible during disruptions, and how adaptive operations affect the value of those investments. Another important theme is trustworthy AI, including uncertainty quantification, physical verification of proposed actions, transparent decision support, and robustness to unseen conditions.
The precise research direction will be shaped jointly with the successful candidate, building on their expertise and the lab’s strengths. The IMOS Lab offers an interdisciplinary environment with collaborations across projects, industrial stakeholders, and leading international universities, and the postdoc will have opportunities to develop an independent research profile and contribute to doctoral supervision.
Funding details
Postdoctoral position at 100% full-time, fixed-term (CDD), duration 1 year renewable; EPFL states competitive salaries.
What's required
A PhD, completed or nearing completion, in Electrical Engineering, Computer Science, Control, Applied Mathematics, Operations Research, or a related field is required. The candidate should have a strong analytical background, 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 research experience in one or more of the following: agentic AI, foundation models, model-based reasoning, graph neural networks, physics-informed learning, 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. Professional command of English, both written and spoken, is mandatory. Candidates should be self-driven, collaborative, creative, and strong problem-solvers.
How to apply
Prepare a motivation letter, CV with publication list, one-page research statement, transcripts of all obtained degrees in English, and contact details for two or three referees. Submit the application through the EPFL application platform.
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Prepare your application
Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.