Foundation Models for Flexible and Safe Multi-Energy Systems
Developing AI-driven foundation models to optimize and secure multi-energy systems in France.
Best for:Best for engineering PhD candidates with expertise in AI, machine learning, or energy systems, interested in developing AI-driven solutions for flexible and resilient energy infrastructure.
Overview
This PhD position focuses on advancing foundation models to enhance the flexibility and safety of multi-energy systems. The project is based in Provence-Alpes-Côte d'Azur, France, and aims to integrate cutting-edge AI techniques into energy infrastructure. The successful candidate will conduct research under academic supervision, contributing to both theoretical and applied advancements in energy systems. The role is ideal for applicants with a strong background in engineering, computer science, or related fields, seeking to explore interdisciplinary solutions for modern energy challenges.
What you'll do
- Develop and implement foundation models to enhance the flexibility and safety of multi-energy systems.
- Conduct theoretical and experimental research to analyze energy system performance and resilience.
- Collaborate with academic and industry partners to integrate AI solutions into real-world energy infrastructure.
- Publish research findings in peer-reviewed journals and present results at international conferences.
- Contribute to the development of innovative methodologies for energy system optimization.
- Participate in interdisciplinary research projects and workshops related to energy and AI.
Requirements
Degree required: Master's degree in Engineering, Computer Science, Physics, or a related quantitative field
Degree offered: PhD
Language: English proficiency; French is not required but may be advantageous
Instruction: English
- Strong background in machine learning or AI techniques
- Experience with programming languages such as Python or MATLAB
- Knowledge of energy systems, control theory, or optimization algorithms
- Familiarity with data analysis and modeling techniques
- Ability to work in interdisciplinary teams
- Open to international applicants
- No specific residency or nationality restrictions mentioned
Funding & terms
How to apply
Deadline: 2026-10-16
Applicants should submit their application through the Campus France portal. Ensure your application includes a detailed CV, a cover letter explaining your research interests, and academic transcripts. Additional documents may be requested depending on the evaluation process.
- CV
- Cover letter
- Academic transcripts
- Research statement or proposal
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