Foundation Models for Flexible and Safe Operation of Multi-Energy Systems

Mines Paris-PSL; France; Hybride; EUR 28 000 - 32 000; Plein temps

Résumé du poste

Mine Paris-PSL is offering a PhD position focused on developing foundation models for multi-energy systems. The researcher will design, train and validate FM surrogates, collect and curate diverse datasets, and apply self-supervised learning to support operational decisions across energy vectors.

The project includes pretraining on electricity, heat, gas and hydrogen data, with Hardware-in-the-Loop validation and collaborations with the PERSEE platform and the France 2030 program under PEPR

Qualifications

• PhD candidate profile: engineer and/or MSc in a relevant field.
• Strong programming skills and scientific curiosity.
• Solid background in AI/ML, data science or optimization.

Responsabilités

• Develop foundation model surrogates for multi-energy systems.
• Construct datasets and explore self-supervised learning.
• Pretrain FM on electricity, heat, gas and hydrogen data and validate via Hardware-in-the-Loop.
• Collaborate with PERSEE and PEPR FutuRE projects.

Connaissances

Artificial intelligence

Data science

Machine learning

Power system management

Renewables integration

Applied mathematics

Statistics and probabilities

Optimization

Energy forecasting

Programming

Formation

Master of Science in Engineering or related field

Outils

Python

MATLAB

Description du poste

Organisation/Company Mines Paris-PSL Research Field Engineering Technology » Energy technology Mathematics Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 15 Oct 2026 - 22:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Nov 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Context and challenges:

The European Union Artificial Intelligence Act defines a Foundation Model (FM) as ‘AI model that is trained on broad data at scale, is designed for generality of output, and can be adapted to a wide range of distinctive tasks’. FMs employ generative AI approaches based on large language models or advanced encoding/decoding architectures, harvesting vast datasets autonomously via self-supervision. FMs show better performance in zero-shot and few-shot learning benchmarks compared with traditional statistical or machine learning models. Relevant developments for the energy sector include so-called Time-Series Foundation Models (TSFM) able to perform forecasting of multiple quantities or sites simultaneously thanks to large pre-trained corpus of diverse datasets. TSFMs tend to be large models, with tens to hundreds of millions of parameters, see e.g. Chronos, TimesFM, Moirai, LLMTime. Conversely, smaller FMs have been dedicated to specific predictive tasks for power systems such as forecasting of renewable power production or electricity prices (WindFM, PowerPM). Alternatives have also been explored to learn better on different resolutions and improve fine-tuning using exogenous information (see e.g. Tiny Time Mixers).

Beyond pure predictions, FM frameworks have been proposed to approximate complex optimization problems in power systems (see e.g. the ongoing GridFM initiative). Leveraging graph-based neural networks help solve optimal power flow with reduced computational cost. However, these frameworks are early-stage and remain limited to electricity-focused applications. As these approaches ignore dynamics in multi-energy operation between energy vectors (e.g. electricity / heat / gas, hydrogen), they cannot guarantee feasible and secure combined operation of electricity, heat and gas systems.

Main objective of the thesis:

This PhD explores the development of foundation models (FMs) as surrogate models for multi-energy system optimization to support operational decisions such as flexibility booking and security-constrained optimal power and energy flows.The development will be supported by two key technical objectives: (1) construction of relevant datasets, starting with the identification and collection of adequate datasets, and complemented after with synthetically generated datasets to enhance the FM learning capabilities; (2) establish a learning approach to extract structural information in a self-supervised manner and fine-tune to downstream tasks for multi-energy systems.

Methodology and expected results:

The proposed FM will be pretrained on large datasets of operational scenarios covering electricity, heat, gas, hydrogen, storage, and conversion technologies, representative of current and future European infrastructures. These datasets will be augmented by a multi-energy flow model developed at PERSEE and through Hardware-in-the-Loop (HIL) experiments. The FM will employ physics-inspired Machine Learning to guarantee physically feasible operation. More generally trustworthy AI principles including explainability and uncertain quantification will be embedded. After pretraining, the architecture will be refined to better represent interactions among energy vectors, investigating among others mixture of experts and fine-tuning following prescriptive analytics principles. The trained FM will act as a fast, high-fidelity surrogate for predictive management and distributed or federated learning experiments, avoiding repeated full multi-energy flow computations. Integration with a HIL platform will validate predictions in realistic operational settings, enabling real-time interaction with controllers and devices.

The expected outcome is a FM-based framework that complements real-time control and predictive management by providing rapid, reliable, and generative support for scenario exploration and operational decision-making across time scales from milliseconds to days.

Collaborations:

This thesis is supported by the PEPR program FutuRE funded underFrance 2030:

The project is also linked to our participation to the European initiative AI.Grids supported by CRESYM and the European Commission.

Profile: Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).

Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming. A succesful candidate will have a solid background in two or more of the following competencies:

• artificial intelligence,data science, machine learning
• power system management, integration of renewables
• applied mathematics, statistics and probabilities
• optimisation, energy forecasting