Postdoc: Physics-Grounded AI for Power-Grid Resilience

EPFL; Lausanne; Sur place; CHF 65 000 - 90 000; Plein temps

Avantages offerts par ce poste

International research environment

Résumé du poste

EPFL in Lausanne invites a motivated postdoctoral researcher to work on Physics-Grounded Agentic AI for Power-System Resilience Across Timescales at the IMOS Lab. The role is 100% in Lausanne, starting December 2026 or upon agreement, for a fixed-term contract with a focus on adaptive decision-making and uncertainty quantification.

The project combines agentic AI, physics-informed learning, GNNs, and MARL, exploring planning-operation links and robust interventions with independence and

Qualifications

• PhD in a related field or near completion.
• Strong foundation in machine learning or optimization.
• Excellent programming skills and ability to conduct independent research.

Responsabilités

• Conduct cutting-edge research on physics-informed AI for power-system resilience across timescales.
• Develop ML methods with graph-based models and MARL for grid applications.
• Collaborate with the IMOS Lab and external partners; publish results and contribute to supervision.

Connaissances

Agentic AI

Graph neural networks

Multi-agent reinforcement learning

Machine learning

Programming

Formation

PhD in Electrical Engineering / Computer Science / Mathematics

Outils

Python

TensorFlow/PyTorch

Git

Description du poste

EPFL in Lausanne invites a motivated postdoctoral researcher to work on Physics-Grounded Agentic AI for Power-System Resilience Across Timescales at the IMOS Lab. The role is 100% in Lausanne, starting December 2026 or upon agreement, for a fixed-term contract with a focus on adaptive decision-making and uncertainty quantification.

The project combines agentic AI, physics-informed learning, GNNs, and MARL, exploring planning-operation links and robust interventions with independence and