Junior research scientist in ecophysiology of multi-stress interactions based on machine 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

You will conduct your activities within the MAGE team (Modelling and Analysis of the Genotype-Environment interaction) of the LEPSE joint research unit. You will draw on the methods, tools, and platforms developed within LEPSE (e.g. M3P phenotyping platforms, SiriusQuality crop model). You will benefit from the team’s long-term collaborative environment and will regularly interact with (i) national and international laboratories with skills in statistics and modelling through machine learning (Agronomy joint research unit, MIA joint research unit, Wageningen University & Research); (ii) national and international laboratories and institutes notably producing large datasets, and technical institutes and partner examination offices.
You will help address a major challenge: increasing crop resilience to cope with the effects of climate change – extreme events, multitude of stresses, growing complexity of scenarios – while maintaining or increasing yield and reducing the use of inputs. For this, you will draw on the genetic variability of plant responses to agro-environmental constraints and the modelling of these effects to identify optimal allele/trait/character x management combinations adapted to local agro-climatic conditions exposed to climate change. Your research will be at the interface between the exploitation of massive datasets (e.g. satellite-based data, varietal assessment networks, targeted experiments), machine learning methods, and dynamic crop modelling. The objective will be to generate new ecophysiological knowledge relating to plant responses to conditions of stress that are little studied as they are rare or challenging to test experimentally, such as interactions between multiple stresses – biotic and abiotic, extreme and/or repeated – to assess the resilience and pertinence of new varieties and agricultural practices in increasingly constrained agroevironments.
You will be in charge of designing a conceptual framework and hybridising machine-learning methods using large datasets (historical databases, trial networks, remote sensing, ad-hoc experiments in controlled conditions or in the field) with crop modelling (to improve biological interpretability) to produce knowledge and laws of responses to extreme and rare types of agro-environmental scenarios. You will start by focussing on identification of the ecophysiological responses of wheat to abiotic and biotic stress within the context of extreme climate events. Excess water, still poorly integrated into current models, will be a priority case study. This stress gives rise to complex effects including soil saturation, root asphyxia, lodging, and disease development, which impact plastic physiological processes that are difficult to capture using traditional approaches. The work will involve identifying the processes most impacted, the targeted response variables, and the prior knowledge to be harnessed, as well as the datasets required to formulate new response laws that can be integrated into a hybrid modelling approach. For that, you will draw more particularly on extensive historical European-scale datasets (specifically from European projects H2020 INVITE and INOVAR), in which these effects have already been identified.

Training and skills

Competition open to candidates with a PhD (or equivalent). 
Sound experience in agronomy/ecophysiology or genetics of plants under stress, as well as an active and proven use of machine learning or more broadly data-driven approaches, is highly recommended. 
Among the skills highly desired, you have shown (i) good theoretical and practical knowledge of the response processes to abiotic constraints (e.g. temperature, water shortage, combined or repeated stresses, complex scenarios); (ii) skills in modelling dynamic processes (e.g. development, growth or transpiration); (iii) theoretical and practical skills in the use of machine learning supported by (iv) sound programming skills (R, Python, C#, etc.).
You have also shown good communication skills to establish the collaborations required for the integration into a wider approach involving numerous internal and external collaborations. 
You are independent, take initiative, have leadership skills, and have demonstrated your ability to disseminate your work in international journals. 
Candidates should have a good command of English, and long-term international experience would also be desirable. Successful candidates who have not yet acquired this experience abroad will be required to do so after their probationary period (1st year).

Your future role explained in video

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. 

How to apply

1. I download the applicant guide Guide for applicants 2026 pdf - 1.41 MB
2. I write down the profile number CR26-AgroEcoSystem-6
3. I apply GO