Missions

12-month renewable fixed-term contract (CDD), with a possible 6-month extension.
The objective of this short-term research position is to advance our understanding of how plants absorb nanoparticles through their leaves by applying machine learning techniques. The researcher will contribute to building a database (from both scientific literature and results from the ERC StG LEAPHY project) and to developing predictive models that integrate the physicochemical properties of nanoparticles, plant leaf surface characteristics, and growth conditions. This work aims to generate leverageable knowledge for the safe and effective use of nanomaterials in agriculture.
Interested applicants are invited to submit a short research project outlining some ideas for work on this theme when they apply online.

Activités

The researcher will be responsible for the following activities:
-Literature Review and Data Collection: Conduct an exhaustive review of current scientific literature on foliar absorption of nanoparticles by plants, as well as data collected from an internal research project. Gather and organize relevant datasets, including published experimental results and open-access databases, ensuring data quality and completeness for model development.
-Database Preparation: Identify and extract key features expected to influence foliar absorption and in-plant mobility. Prepare the dataset for machine learning analysis.
-Model Development and Validation: Select, implement, and apply machine learning models to predict nanoparticle foliar absorption. Train models, perform cross-validation and sensitivity analysis to assess model accuracy and identify the most influential factors.
-Development of a Conceptual Framework from the Models: Attempt to build a conceptual framework from the modeling results to provide predictive tools for studying the fate of nanomaterials after foliar deposition.
-Team Collaboration on Scientific Dissemination: Work with the team to write scientific publications and present results at international conferences.

Compétences

• PhD in Environmental Geosciences, Environmental Engineering, Materials Chemistry, or Data Science/Artificial Intelligence
• Experience and autonomy in analyzing complex datasets using machine learning approaches
• Experience or strong interest in nanoparticle–plant interactions and the environmental fate of nanomaterials
• Proactive contributor in a multidisciplinary team
• Strong written and oral communication skills
• Fluency in English is essential (written and spoken)

Contexte de travail

The research will be carried out at the Géosciences Environnement Toulouse (GET) laboratory, part of the Observatoire Midi-Pyrénées. GET includes 250 staff, of which around 150 are researchers or teacher-researchers. It offers a multidisciplinary environment involving geologists, chemists, physicists, biologists, modelers, and sociologists. The lab hosts numerous experimental, analytical, and computational platforms supporting the project.
https://www.get.omp.eu/en/geosciences-environnement-toulouse-en/
This postdoctoral work will be part of the ERC StG LEAPHY project, which started in September 2022 and is led by Astrid Avellan. The grant will cover operational costs for the project.
More information: https://www.get.omp.eu/g-cube/leaphy/
Occasional travel to major research facilities in Europe or other laboratories, as well as participation in workshops and/or international conferences will be possible.

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.

Contraintes et risques

Short trips within France and/or abroad are to be expected.