PhD in Federated Predictive Management of Multi-Energy Systems with Privacy Preservation

This PhD position focuses on advancing federated predictive management techniques for multi-energy systems while ensuring strict privacy preservation. The project is hosted by a research group affiliated with the Doctoral School of Materials and Mechanics, Energy and Processes, Mathematics and Systems (ED 621) in Provence-Alpes-Côte d'Azur, France. The successful candidate will contribute to cutting-edge research in energy systems, combining predictive analytics with privacy-enhancing technologies. The role involves designing algorithms, conducting simulations, and validating solutions in real-world or simulated multi-energy environments. Applicants should be passionate about interdisciplinary research at the intersection of energy systems, computer science, and privacy-preserving methods.

• Develop and implement federated predictive management algorithms for multi-energy systems.
• Design privacy-preserving techniques to ensure data confidentiality and security in distributed environments.
• Conduct simulations and experiments to validate the performance and robustness of proposed methods.
• Collaborate with interdisciplinary teams to integrate predictive models into real-world energy systems.
• Publish research findings in high-impact academic journals and conferences.
• Present research progress at internal and external seminars and workshops.