Post-doctoral researcher in energy systems management (Optimization and Artificial Intelligence)

IMT Mines Albi; France; Hybride; EUR 35 000 - 45 000; Plein temps

Résumé du poste

A prestigious engineering school in France is seeking a Postdoc Researcher for a 12-month project focused on developing an intelligent control system for thermal energy storage. The successful candidate will have a PhD in engineering or mathematics, excellent communication skills, and knowledge of Matlab or Python. The role offers opportunities for teleworking and participation in scientific activities.

Prestations

Teleworking possible

On-site restaurant and cafeteria

Accessible by public transport

Qualifications

• PhD obtained within the last three years.
• In-depth scientific knowledge in energy storage.
• Ability to work in a team with academic and industrial partners.

Responsabilités

• Carry out research tasks specified in the Stock-HD project.
• Contribute to the reputation of the Stock-HD project and IMT Mines Albi.

Connaissances

Excellent scientific communication skills

Fluency in written and spoken English

Knowledge of software development tools (Matlab/Python)

In-depth knowledge of optimization techniques

In-depth knowledge of reinforcement learning techniques

Formation

PhD in engineering or mathematics and automation

Outils

Matlab

Python

Description du poste

Organisation/Company IMT Mines Albi Department 81 Research Field Engineering Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Country France Application Deadline 31 Dec 2025 - 00:00 (Europe/Paris) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

• WORK ENVIRONMENT

IMT Mines Albi, a prestigious school under the Ministry of Industry, is part of the Institut Mines-Télécom, France's leading group of engineering and management schools. IMT Mines Albi's positioning in terms of training and research makes it a benchmark school for the industry of the future, energy, the circular economy, and health and well-being engineering.

RAPSODEE Research Centre, a joint research unit of the CNRS, is one of three research centres at IMT Mines Albi. RAPSODEE is a laboratory with around 100 staff members conducting research in several fields, such as process engineering, energy and energy system optimisation.

Context

The ANR Stock-HD research project brings together two academic laboratories and a company. The scientific challenge of the project is to develop a high-density thermochemical heat storage system at temperatures compatible with integration into a heating network. The various stages involve designing, building and testing an innovative heat storage process, and characterising its behaviour and performance. The researcher recruited will be involved in the stage of the project concerning the intelligent control of the storage system.

Objectives

The main objective of this 12-month research contract is to design an intelligent control system for the original heat storage system developed as part of the Stock-HD project, integrated into a heating network. Intelligent control systems must be capable of predicting consumer demand profiles and adapting to any changes in the network, be robust in the face of uncertainty, and remain flexible. In recent years, new strategies for the optimal management of distributed systems, based on analysis, simulation, decision‑making and control using data, have been proposed. For control or management applications, reinforcement learning (RL/DRL), a branch of machine learning, is a promising solution that involves training an autonomous agent to ‘learn’ a control strategy. This formalism is similar to that of optimal control, with the difference that the agent does not have an explicit model of the dynamics of the system to be controlled. This RL approach will be compared and contrasted with optimal control methods such as Model Predictive Control (MPC). The various control strategies developed will first be evaluated on a complete simulation model of the storage system currently being developed in Stock-HD, then on a laboratory pilot installation.

Missions

• Carry out the research tasks specified in Stock-HD project
• Contribute to the reputation of the Stock-HD project and IMT Mines Albi

Main Activities

1. Carry out the research tasks specified in Stock-HD project
   • Develop a reduced model for the control system design.
   • Develop an MPC‑based control system (benchmark) on a simulator.
   • Develop an RL/DRL‑based control system on a simulator.
   • Comparison and evaluation of the different approaches.
   • Implementation of the chosen approach on the laboratory installation.
2. Contribute to the reputation of the Stock-HD project and IMT Mines Albi
   • Publish the results of the research work.
   • Give presentations and lectures.
   • Participate in scientific activities.

Minimum level of education and/or experience required

PhD in engineering or mathematics and automation or equivalent, obtained within the last three years

Essential skills, knowledge and experience

• Excellent scientific communication skills.
• Fluency in written and spoken English.
• Knowledge of software development tools and environments: Matlab and/or Python.
• In‑depth scientific knowledge of optimisation and/or reinforcement learning techniques.

Desirable skills, knowledge and experience

• In‑depth scientific knowledge in energy storage
• In‑depth scientific knowledge in reinforcement learning

Skills and abilities

• Ability to work in a team with academic and industrial partners
• Interpersonal skills
• Responsiveness, initiative and rigour

Other Information

• Working conditions de travail : teleworking possible, on-site restaurant and cafeteria, accessible by public transport en transport en commun

A detailed CV, including your educational background, a complete list of publications, awards, etc.

A cover letter of no more than two pages describing your interests, your objectives and how they fit in with this project.

A copy of your most relevant publication.

Three references (names, email addresses, mention of your professional relationship with each of them)

Any other information deemed useful for the review of your application.