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

Develop AI methods for power-system resilience across infrastructure planning, operational decisions and disruption response in the Intelligent Maintenance and Operations Systems Lab. Relevant approaches include foundation models, physics-informed graph learning and multi-agent reinforcement learning under constraints and uncertainty. The full-time fixed-term appointment in Lausanne is planned from 1 December 2026 or by agreement, initially for one year with possible renewal.

Strong machine-learning or optimization research and programming skills; experience in agentic AI, graph neural networks, physics-informed modelling, reinforcement learning or related methods. Power-system knowledge is an asset. Professional English is required. Apply online with a motivation letter, CV/publications, one-page research statement, English degree transcripts and two or three referees.