Federated AI for Privacy-Preserving Multi-Energy Ops

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

Avantages offerts par ce poste

PhD stipend

Open-source tools

Résumé du poste

MINES Paris-PSL invites applications for a PhD thesis on Privacy-Preserving Federated Predictive Operation of Multi-Energy Systems. The project develops distributed AI to coordinate generation, storage and flexible demand while keeping data confidential.

You will explore Federated Learning, multi-agent coordination and robust optimization inside an open-source simulation environment, delivering a practical framework for privacy-aware energy management.

Qualifications

• Master's degree in a relevant field (engineering, applied mathematics, data science).
• Strong programming and analytical skills; experience with ML/AI techniques.

Responsabilités

• Develop privacy-preserving federated AI methods for multi-energy systems.
• Implement distributed optimization and multi-agent coordination strategies.
• Evaluate algorithms in an open-source simulation environment and contribute to a transferable framework.

Connaissances

AI

data science

machine learning

programming

Formation

Master of Science

Outils

Python

MATLAB

R

Description du poste

MINES Paris-PSL invites applications for a PhD thesis on Privacy-Preserving Federated Predictive Operation of Multi-Energy Systems. The project develops distributed AI to coordinate generation, storage and flexible demand while keeping data confidential.

You will explore Federated Learning, multi-agent coordination and robust optimization inside an open-source simulation environment, delivering a practical framework for privacy-aware energy management.