MINES Paris - PSL
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PhD in Privacy-Preserving Federated Predictive Operation of Multi-Energy Systems Mines Paris-PSL in France
Degree Level
PhD
Field of study
Computer Science
Funding
PhD funded under the PEPR program FutuRE (France 2030), with other public funding. The project is also linked to the European project Fair4Communities.
Deadline
Oct 23, 2026
Country
France
University
MINES Paris - PSL
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About this position
PhD opportunity at Mines Paris-PSL, Centre PERSEE / ERSEI group, in Sophia Antipolis, France. The thesis is titled Privacy-Preserving Federated Predictive Operation of Multi-Energy Systems and focuses on multi-energy systems, federated learning, privacy-preserving AI, predictive analytics, prescriptive analytics, multi-agent systems, and distributed optimization.
The project addresses the coordinated operation of coupled electricity, thermal, and gas/hydrogen networks in settings such as energy communities, microgrids, residential districts, industrial clusters, and territorial energy systems. The research aims to develop distributed and federated AI methods that allow multiple actors to collaborate without centralizing sensitive operational data, while maintaining robustness, explainability, uncertainty quantification, and privacy.
The PhD will combine forecasting and decision-making for energy demand, renewable generation, flexibility, storage, and flexible consumption. It will explore federated learning beyond conventional forecasting, integrating it into a decision framework for predictive and prescriptive management. Coordination among actors will use multi-agent architectures, game theory, and distributed optimization.
Funding: supported by the PEPR FutuRE program under France 2030, with other public funding. The project is also linked to the European project Fair4Communities.
Eligibility: engineer and/or Master of Science degree; applicants may apply before the degree is obtained, but the PhD starts only after successful completion. Strong programming skills and a background in at least two of AI/data science/machine learning, applied mathematics/statistics, optimisation, and power systems are expected. Good French is desirable; excellent English is required.
Application: submit a CV, motivation letter, transcripts, last diploma, and contact details for two referees. Use the email subject acronym PHD-2026-ERSEI-FUTURE-FLOperation. Contact Prof. Georges Kariniotakis, with copy to Dr. Sergio-Daniel Montana-Salas. Deadline: 2026-10-23.
Funding details
PhD funded under the PEPR program FutuRE (France 2030), with other public funding. The project is also linked to the European project Fair4Communities.
What's required
Engineer and/or Master of Science degree; candidates may apply before obtaining the master's degree, but the PhD will start only after the degree is successfully obtained. Applicants should have a good general and scientific culture, strong analytical, synthesis, innovation, communication, adaptability, and creativity skills, motivation for research, a coherent professional project, and programming skills. A successful candidate should have a solid background in at least two of the following: artificial intelligence, data science, machine learning, applied mathematics, statistics and probabilities, optimisation, and power systems. Good French is desirable and excellent English is required.
How to apply
Send a CV, motivation letter, copies of grade transcripts and last diploma, and contact details for two referees (or reference letters if already available). Use the email subject acronym "PHD-2026-ERSEI-FUTURE-FLOperation" and contact Prof. Georges Kariniotakis, copying Dr. Sergio-Daniel Montana-Salas.
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