Post-Doc Position: Safe AI Planning and Reinforcement Learning using Formal Methods

CNRS; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A leading research organization in France is offering a postdoc position in AI combining formal methods and reinforcement learning. Located in Rennes, this role involves developing safe algorithms and collaborating with Inria and Mitsubishi Electric R&D Centre. The ideal candidate should have a strong theoretical background in MDPs and possess good communication skills. Competitive salary and flexible working hours are provided.

Prestations

Partial reimbursement of public transport costs

7 weeks of annual leave + extra 10 days off

Possibility of teleworking

Professional equipment available

Access to vocational training

Qualifications

• Strong theoretical background on MDPs required.
• Good communication skills essential.
• Mastery of AI planning and reinforcement learning is advantageous.

Responsabilités

• Develop safe planning and reinforcement learning algorithms.
• Collaborate with researchers from Inria and MERCE.
• Validate algorithms experimentally on case studies.

Connaissances

Background in probability, Markov chains, MDPs

Knowledge about reinforcement learning and planning

Good level of English

Good communication and reporting skills

Interest in collaborative work

Description du poste

Organisation/Company CNRS Department Computer Science Research Field Computer science Researcher Profile First Stage Researcher (R1) Recognised Researcher (R2) Positions Postdoc Positions Country France Application Deadline 1 Jan 2026 - 12:00 (Europe/Brussels) Type of Contract Temporary Job Status Full-time Hours Per Week 38.5 Offer Starting Date 3 Jan 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

The post-doc position is part of a collaboration between Inria and Mitsubishi Electric R&D Centre Europe (MERCE) within the FRAIME project on artificial intelligence and formal methods. The project explores, on the one hand, how Formal Methods can provide guarantees on AI systems, and on the other hand how AI can help Formal Methods to be more efficient and easier to use by practitioners. The vision is to intertwine Formal Methods and AI to efficiently design safe systems. This is a postdoctoral position in the fields of AI planning, reinforcement learning (RL), and formal methods. The position is initially funded for 12 months, but it is further extensible to at least another year. While this is an academic position based at Inria Rennes, the candidate will collaborate with researchers from both Inria and MERCE, thus benefiting from both academic and industrial research environments. The work will be done in collaboration with Nathalie Bertrand and Ocan Sankur (Inria DEVINE team https://devine.inria.fr/ ) and Benoît Boyer (MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop algorithms for multi-environment MDPs, partially observable MDPs, and their variants and apply these in appropriate applications provided by MERCE. We will focus on developing practical solutions for these formalisms. Some possibilities are to develop solutions based on dynamic programming over finite horizon, or using mathematical solvers, or adapting reinforcement learning algorithms to the desired context. Furthermore, the candidate can also study theoretical properties of the developed algorithms such as their complexity, optimality, and measures such as the regret. These algorithms are expected to be validated experimentally on appropriate case studies. The overall objective is to contribute to the state of the art of planning and RL algorithms with strong safety guarantees. Some references: - Sun et al. Online MDP with Prototypes Information: A Robust Adaptive Approach. AAAI 2025. - Royer et al. Multiple-environment markov decision processes: Efficient analysis and applications. ICAPS 2020. - Chatterjee et al. The Value Problem for Multiple-Environment MDPs with Parity Objective. ICALP 2025.

Advantages

• Partial reimbursement of public transport costs
• Leave: 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 (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training

The candidate can start anytime during the 2025-2026 academic year.

Salary: Monthly gross salary of 2788 euros

Qualifications

• Background in probability, Markov chains, MDPs. Knowledge about reinforcement learning and planning are a plus but not necessary for candidates with a strong theoretical background on MDPs.
• Good level of English
• Good communication and reporting skills
• An interest in collaborative work

Research Field

Computer science

Additional Information

Work Location(s)

Number of offers available 1 Company/Institute IRISA, Inria, CNRS, Université de Rennes Country France City Rennes Postal Code 35000 Geofield