About the research centre or Inria department

Inria job offers >Offer #2026-10268 Post-Doctoral Research Visit F/M Postdoctoral Fellowship – PGMO Project: From Complementarity to Multi-Agent Learning for Electricity Markets Download job offer in PDF format Contract type : Fixed-term contract Renewable contract : Yes Level of qualifications required : PhD or equivalent Fonction : Post-Doctoral Research Visit About The Research Centre Or Inria Department Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region. For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT). Context This postdoctoral position is part of a project funded by the PGMO program (Gaspard Monge Program for Optimization and Operations Research). It aims to develop new modeling and optimization methods to address the challenges of the energy transition. The project lies at the intersection of operations research, mathematical optimization, game theory, and artificial intelligence, with applications to electricity markets. The work will be conducted in collaboration with academic researchers and industrial partners, notably EDF, to develop decision-support tools for energy policy. Assignment This project examines the investment decisions of electricity generators in decentralized markets subject to uncertainty. It models interactions among generators using stochastic equilibrium games that incorporate long-term contracts, such as capacity mechanisms and contracts for difference (CfDs). The goal is to better understand how these contracts influence investments and the composition of the energy mix. The project combines two complementary approaches: optimization methods based on complementarity problems and multi-agent learning techniques. The latter allow for approximating equilibria when models become too complex for classical methods. The research also takes into account market power, risk aversion, and the presence of multiple equilibria. Finally, the models will be applied to real-world cases, particularly island power systems, to assess the economic impacts and implications for energy policies. Main activities Here is a summary of the work program in a few points: WP1 – Model Development: Incorporate long-term contracts, uncertainty, risk, and market power into a stochastic equilibrium model.WP2 – Algorithm Development: Design and compare multi-agent learning methods to calculate equilibria in complex electricity markets.WP3 – Economic analysis: Evaluate the impact of different contracts on investments, the energy mix, and market efficiency using real-world case studies.Validation: Compare the new approaches to traditional optimization methods in terms of accuracy, robustness, and scalability.Dissemination: Publish the results in scientific conferences and journals, in collaboration with industrial partners such as EDF. Skills The candidate must hold a Ph.D. in applied mathematics, operations research, or optimization. The Following Skills Will Be Particularly Valued: Solid knowledge of mathematical optimization, game theory, or operations research.Strong skills in mathematical modeling and algorithm development.Interest in machine learning, particularly multi-agent learning, or a willingness to develop skills in this area.Experience with electricity markets, energy systems, or optimization under uncertainty is a plus, though not required.Proficiency in at least one scientific programming language (Python, Julia, MATLAB, or equivalent).Ability to conduct research independently while working effectively within a multidisciplinary team.Strong writing and scientific communication skills in English (written and oral), sufficient to publish in international journals and present work at conferences.Motivation to collaborate with academic and industrial partners and contribute to the development of decision-support tools for the energy transition. Benefits package Subsidized mealsPartial reimbursement of public transport costsLeave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)Possibility of teleworking and flexible organization of working hoursProfessional equipment available (videoconferencing, loan of computer equipment, etc.)Social, cultural and sports events and activitiesAccess to vocational trainingSocial security coverage Remuneration €2,788 gross per month Apply for this position Share Facebook Linkedin Twitter Email General Information Theme/Domain : Optimization, machine learning and statistical methods Statistics (Big data) (BAP E)Town/city : Villeneuve d'AscqInria Center : Centre Inria de l'Université de Lille Starting date : 2026-10-01Duration of contract : 12 monthsDeadline to apply : 2026-08-07 Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed. Instruction to apply Please provide your CV and cover letter Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment. Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities. Contacts Inria Team : INOCS Recruiter : Le Cadre Helene / helene.le-cadre@inria.fr About Inria Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact. Post-Doctoral Research Visit F/M Postdoctoral Fellowship – PGMO Project: From Complementarity to Multi-Agent Learning for Electricity Markets Download job offer in PDF format Contract type : Fixed-term contract Renewable contract : Yes Level of qualifications required : PhD or equivalent Fonction : Post-Doctoral Research Visit About The Research Centre Or Inria Department Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region. For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT). Context This postdoctoral position is part of a project funded by the PGMO program (Gaspard Monge Program for Optimization and Operations Research). It aims to develop new modeling and optimization methods to address the challenges of the energy transition. The project lies at the intersection of operations research, mathematical optimization, game theory, and artificial intelligence, with applications to electricity markets. The work will be conducted in collaboration with academic researchers and industrial partners, notably EDF, to develop decision-support tools for energy policy. Assignment This project examines the investment decisions of electricity generators in decentralized markets subject to uncertainty. It models interactions among generators using stochastic equilibrium games that incorporate long-term contracts, such as capacity mechanisms and contracts for difference (CfDs). The goal is to better understand how these contracts influence investments and the composition of the energy mix. The project combines two complementary approaches: optimization methods based on complementarity problems and multi-agent learning techniques. The latter allow for approximating equilibria when models become too complex for classical methods. The research also takes into account market power, risk aversion, and the presence of multiple equilibria. Finally, the models will be applied to real-world cases, particularly island power systems, to assess the economic impacts and implications for energy policies. Main activities Here is a summary of the work program in a few points: WP1 – Model Development: Incorporate long-term contracts, uncertainty, risk, and market power into a stochastic equilibrium model.WP2 – Algorithm Development: Design and compare multi-agent learning methods to calculate equilibria in complex electricity markets.WP3 – Economic analysis: Evaluate the impact of different contracts on investments, the energy mix, and market efficiency using real-world case studies.Validation: Compare the new approaches to traditional optimization methods in terms of accuracy, robustness, and scalability.Dissemination: Publish the results in scientific conferences and journals, in collaboration with industrial partners such as EDF. The Following Skills Will Be Particularly Valued: Solid knowledge of mathematical optimization, game theory, or operations research.Strong skills in mathematical modeling and algorithm development.Interest in machine learning, particularly multi-agent learning, or a willingness to develop skills in this area.Experience with electricity markets, energy systems, or optimization under uncertainty is a plus, though not required.Proficiency in at least one scientific programming language (Python, Julia, MATLAB, or equivalent).Ability to conduct research independently while working effectively within a multidisciplinary team.Strong writing and scientific communication skills in English (written and oral), sufficient to publish in international journals and present work at conferences.Motivation to collaborate with academic and industrial partners and contribute to the development of decision-support tools for the energy transition. Benefits package Subsidized mealsPartial reimbursement of public transport costsLeave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)Possibility of teleworking and flexible organization of working hoursProfessional equipment available (videoconferencing, loan of computer equipment, etc.)Social, cultural and sports events and activitiesAccess to vocational trainingSocial security coverage Remuneration €2,788 gross per month Apply for this position Share Facebook Linkedin Twitter Email General Information Theme/Domain : Optimization, machine learning and statistical methods Statistics (Big data) (BAP E)Town/city : Villeneuve d'AscqInria Center : Centre Inria de l'Université de Lille Starting date : 2026-10-01Duration of contract : 12 monthsDeadline to apply : 2026-08-07 Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed. Instruction to apply Please provide your CV and cover letter Recruitment Policy : As part of its diversity policy, all Inria positions are accessible to people with disabilities. Contacts Inria Team : INOCS Recruiter : Le Cadre Helene / helene.le-cadre@inria.fr About The Research Centre Or Inria Department Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region. For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT). Context This postdoctoral position is part of a project funded by the PGMO program (Gaspard Monge Program for Optimization and Operations Research). It aims to develop new modeling and optimization methods to address the challenges of the energy transition. The project lies at the intersection of operations research, mathematical optimization, game theory, and artificial intelligence, with applications to electricity markets. The work will be conducted in collaboration with academic researchers and industrial partners, notably EDF, to develop decision-support tools for energy policy. Assignment This project examines the investment decisions of electricity generators in decentralized markets subject to uncertainty. It models interactions among generators using stochastic equilibrium games that incorporate long-term contracts, such as capacity mechanisms and contracts for difference (CfDs). The goal is to better understand how these contracts influence investments and the composition of the energy mix. The project combines two complementary approaches: optimization methods based on complementarity problems and multi-agent learning techniques. The latter allow for approximating equilibria when models become too complex for classical methods. The research also takes into account market power, risk aversion, and the presence of multiple equilibria. Finally, the models will be applied to real-world cases, particularly island power systems, to assess the economic impacts and implications for energy policies. Main activities Here is a summary of the work program in a few points: WP1 – Model Development: Incorporate long-term contracts, uncertainty, risk, and market power into a stochastic equilibrium model.WP2 – Algorithm Development: Design and compare multi-agent learning methods to calculate equilibria in complex electricity markets.WP3 – Economic analysis: Evaluate the impact of different contracts on investments, the energy mix, and market efficiency using real-world case studies.Validation: Compare the new approaches to traditional optimization methods in terms of accuracy, robustness, and scalability.Dissemination: Publish the results in scientific conferences and journals, in collaboration with industrial partners such as EDF. The Following Skills Will Be Particularly Valued: Solid knowledge of mathematical optimization, game theory, or operations research.Strong skills in mathematical modeling and algorithm development.Interest in machine learning, particularly multi-agent learning, or a willingness to develop skills in this area.Experience with electricity markets, energy systems, or optimization under uncertainty is a plus, though not required.Proficiency in at least one scientific programming language (Python, Julia, MATLAB, or equivalent).Ability to conduct research independently while working effectively within a multidisciplinary team.Strong writing and scientific communication skills in English (written and oral), sufficient to publish in international journals and present work at conferences.Motivation to collaborate with academic and industrial partners and contribute to the development of decision-support tools for the energy transition. Benefits package Subsidized mealsPartial reimbursement of public transport costsLeave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)Possibility of teleworking and flexible organization of working hoursProfessional equipment available (videoconferencing, loan of computer equipment, etc.)Social, cultural and sports events and activitiesAccess to vocational trainingSocial security coverage Remuneration €2,788 gross per month