Junior research scientist in reinforcement learning

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INRAE presentation

INRAE, the French National Research Institute for Agriculture, Food, and Environment, is a public research organization bringing together 12,000 employees across 272 units in 18 centers across France. As the world’s leading institute specializing in agriculture, food, and the environment, INRAE plays a key role in supporting the necessary transitions to address global challenges.

Faced with population growth, food security challenges, climate change, resource depletion, and biodiversity loss, INRAE is committed to developing scientific solutions and supporting the evolution of agricultural, food, and environmental practices.

INRAE is recruiting researchers by open competition and offering permanent position.

Work environment, missions and activities

The Mathematics and Computer Science for Complex Systems (MIAT) research unit is part of the INRAE’s Mathematics, Computer Science, Data Science, and Digital Technologies (MathNum) research department. The unit is composed of two research teams (SCIDyn and SaAB) and three service teams (GENOTOUL Bioinfo, RECORD, and SIGENAE platforms). 
You will be positioned within the SCIDyn (Simulation, Control and Inference of Agro-environmental and Biological Dynamics) team, which consists of nine researchers and engineers, primarily from the fields of computer science and statistics. One of the main research areas of the SCIDyn team is reinforcement learning (RL), a rapidly expanding field in designing decision models for agriculture, forestry, etc. Classical RL approaches are applicable when a model of the system to be controlled is available; however, when only observational data of the system are accessible, inverse RL approaches are required. You will contribute to strengthening the team's expertise in reinforcement learning in this area, with a mission to develop innovative RL approaches and algorithms in collaboration with SCIDyn researchers, within a context where experiments are costly but data of varying types, quality, and complexity are available from our agronomist and ecologist partners. 

In particular, your research project will aim to develop and implement RL algorithms using observed trajectories of controlled systems, where observations come from various sources: direct experiments, simulation, observations of other agents, experiments on different agroecosystems, etc. You will explore one or more areas of RL (Inverse RL, Batch RL, imitation RL, Deep RL, etc.) in alignment with the SCIDyn team’s finalized objectives, with the support of team researchers skilled in single and multi-agent RL. 

You will benefit from the wide collaboration network already existing in MIAT and you will expand it in different directions. You will participate in ongoing projects and develop your own to elaborate support decision methods based on advanced RL.

Training and skills

You hold a doctorate or equivalent qualification. Specialization in reinforcement learning is highly recommended.
You have demonstrated your ability to develop computer-based methods and algorithms. An interest in applied research and targeted applications would be a plus. An ability to exchange and build with specialists in applied disciplines will be expected. You should have a collaborative spirit, strong interpersonal skills, and demonstrate initiative, autonomy, and dynamism. 
Proficiency in English is essential, and long-term international experience is desired; successful candidates without prior international experience will be strongly encouraged to undertake a stay abroad, co-designed with the host team, following the first year of the position.

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INRAE's life quality

By joining our teams, you benefit from:

- 30 days of annual leave + 15 days "Reduction of Working Time" (for a full time);
- parenting support: CESU childcare, leisure services;
- skills development systems: training, career advise;
- social support: advice and listening, social assistance and loans;
- holiday and leisure services: holiday vouchers, accommodation at preferential rates;
- sports and cultural activities;
- collective catering.

For international scientists: please visit your guide to facilitate your arrival and stay at INRAE.