Foundation AI for Safe, Flexible Multi-Energy Operations

MINES Paris PSL; Valbonne; Hybride; EUR 20 000 - 27 000; Plein temps

Résumé du poste

MINES Paris-PSL invites applications for a PhD position focused on Foundation Models for Flexible and Safe Operation of Multi-Energy Systems. The research aims to develop FM surrogates for operational decisions, create relevant datasets, and apply self-supervised learning to electrical, heat, gas, and hydrogen networks.

The project integrates physics-informed ML, uncertainty quantification, and distributed learning, with validation on Hardware-in-the-Loop platforms and real-time control

Qualifications

• PhD eligibility or ongoing PhD in engineering/energy systems.
• Strong background in AI/ML for energy systems preferred.
• Experience with datasets and self-supervised learning helpful.

Responsabilités

• Develop foundation models as surrogates for multi-energy optimization.
• Identify and assemble datasets, including synthetic data for training.
• Experiment with physics-informed ML and HIL validation.
• Ensure explainability and uncertainty quantification in models.
• Collaborate with PERSEE and European initiatives for dissemination.

Connaissances

Artificial intelligence

Data science

Machine learning

Power system management

Renewable integration

Optimization

Energy forecasting

Programming

Formation

Master of Science in Engineering or related field

Outils

Python

MATLAB

PyTorch

Description du poste

MINES Paris-PSL invites applications for a PhD position focused on Foundation Models for Flexible and Safe Operation of Multi-Energy Systems. The research aims to develop FM surrogates for operational decisions, create relevant datasets, and apply self-supervised learning to electrical, heat, gas, and hydrogen networks.

The project integrates physics-informed ML, uncertainty quantification, and distributed learning, with validation on Hardware-in-the-Loop platforms and real-time control