Job Information
Organisation/Company: INRAE
Department: AgroEcoSystem
Research Field: Computer science » Other; Biological sciences; Agricultural sciences
Researcher Profile: First Stage Researcher (R1)
Positions: PhD Positions
Application Deadline: 31 Oct 2026 - 12:00 (Europe/Paris)
Country: France
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 35
Offer Starting Date: 1 Nov 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
Research context and objectives
Agriculture needs to diversify in order to adapt to climate change and other global environmental challenges. Among the diversity of cultivated plants, some species or varieties may display unusual combinations of functional traits associated with original ecological strategies and potentially valuable agronomic properties. Identifying these rare phenotypes could therefore reveal new opportunities for crop diversification.
However, plant phenotypic information is currently scattered across thousands of scientific articles, databases and technical resources. Integrating these heterogeneous sources creates a major challenge: an unusual observation may represent either a genuine biological singularity or an artefact resulting from measurement, annotation, extraction or data-integration errors.
This PhD project lies at the interface between artificial intelligence, functional ecology and agroecology. Its main objective is to develop explainable AI methods capable of discovering rare crop phenotypes and assessing their scientific credibility.
The project will address three closely connected research challenges.
1. Building knowledge from heterogeneous scientific sources
The PhD candidate will contribute to the development of CropTraits, a global database of functional traits of cultivated plants. Large Language Models (LLMs), scientific text mining and knowledge representation approaches will be investigated to extract information from the scientific literature, harmonise heterogeneous concepts and variables, connect information from multiple sources, and explicitly represent uncertainty.
The resulting knowledge base will provide a large-scale representation of crop phenotypic diversity.
2. Detecting rare phenotypes in multidimensional trait spaces
Machine-learning approaches for anomaly and rare-event detection will be developed to explore high-dimensional phenotypic spaces and identify observations that depart from dominant crop functional strategies.
A central methodological challenge will be to move beyond conventional outlier detection: unusual observations will not automatically be considered errors, but rather candidate biological discoveries whose credibility needs to be evaluated.
The project will investigate methods combining anomaly scores, uncertainty estimates, similarity structures and biological context to identify potentially rare functional strategies.
3. Explaining whether a rare observation represents a discovery or an error
For each candidate rare phenotype, several competing explanations may exist: genuine biological singularity, measurement error, data-integration error or context-dependent response.
LLMs will be used to retrieve relevant evidence from the scientific literature, associated metadata and the knowledge base. Because this evidence may be incomplete, uncertain or contradictory, the project will develop computational argumentation and reasoning-under-uncertainty approaches to formally compare competing hypotheses.
The aim is not only to classify observations as credible or unreliable, but also to provide a transparent explanation of the reasoning and evidence supporting the conclusion.
Scientific environment
The PhD will be carried out in Montpellier, France, within a highly interdisciplinary research environment involving three complementary research groups:
- LEPSE – INRAE, working on plant ecophysiology, crop diversity and adaptation to environmental constraints;
- CEFE – CNRS / Université de Montpellier / EPHE / IRD, with expertise in functional ecology, functional traits and functional rarity;
- LIRMM – CNRS / Université de Montpellier, with expertise in artificial intelligence, knowledge representation, computational argumentation and reasoning with conflicting information.
The PhD will be supervised by Denis Vile (INRAE, LEPSE), Madalina Croitoru (Université de Montpellier, LIRMM) and Lucie Mahaut (INRAE, CEFE) and will be affiliated with the GAIA Doctoral School.
This interdisciplinary setting will allow the candidate to develop methodological advances in AI while addressing an original scientific problem with direct relevance to crop diversification and agroecological transitions.
Expected outcomes
The PhD is expected to contribute both to artificial intelligence methodology and to the understanding of crop functional diversity. It will develop an original framework combining knowledge extraction, rare-event detection and explainable reasoning to distinguish genuine scientific discoveries from errors in massive heterogeneous datasets.
The approaches developed in this project may ultimately be transferable well beyond crop science to other biological, environmental or biomedical knowledge bases facing the same fundamental challenge: how can we distinguish a rare discovery from an error?
Where to apply
E-mail: lucie.mahaut@cefe.cnrs.fr
Requirements
Research Field: Computer science » Other
Education Level: Master Degree or equivalent
Skills/Qualifications
Candidate profile
We are looking for a highly motivated candidate interested in working at the interface between artificial intelligence and biological sciences.
Applicants should hold, or be about to obtain, a Master's degree or equivalent in computer science, artificial intelligence, data science, bioinformatics, computational biology, or a related quantitative field.
Experience or strong interest in several of the following topics would be particularly valuable:
- machine learning and data analysis;
- Large Language Models and Natural Language Processing;
- knowledge representation and knowledge graphs;
- explainable AI and reasoning under uncertainty;
- computational argumentation;
- scientific data integration;
- Python and associated data-science / machine-learning tools.
Candidates from plant science, ecology or agronomy with a strong quantitative and computational background may also be considered.
Beyond technical skills, we are looking for a candidate with strong scientific curiosity, an interest in interdisciplinary research, the ability to critically analyse complex information, and good written and oral communication skills in English.
Languages: FRENCH
Level: Good
Languages: ENGLISH
Level: Excellent
Work Location(s)
Number of offers available: 1
Company/Institute: INRAE
Country: France
City: Montpellier
Contact
City: Montpellier
Website: https://lepse.montpellier.hub.inrae.fr
Street: 2 place pierre viala
Postal Code: 34060
E-Mail: denis.vile@inrae.fr
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