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  "employer_name": "French National Research Institute for Agriculture, Food, and the Environment (INRAE)",
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  "job_description": "French National Research Institute for Agriculture, Food, and the Environment (INRAE)\n\nOrganisation/Company French National Research Institute for Agriculture, Food, and the Environment (INRAE) Department 0729 MISTEA Research Field Mathematics Researcher Profile Recognised Researcher (R2) Application Deadline 5 Mar 2026 - 23:59 (UTC) Country France Type of Contract Permanent Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Sep 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\n\nOffer Description\n\nYou will work within the MISTEA unit based in Montpellier. MISTEA develops original methods in mathematics, computer science, and statistics applied to the environment and agronomy. Our main objective is to contribute to a better understanding of and provide decision support on issues related to agriculture and the management and protection of natural resources. MISTEA members have expertise in algorithms and stochastic processes, statistics, filtering and observers, optimization and segmentation, and their work addresses key challenges in digital livestock farming, bioprocesses, and phenotyping in sensor data processing. You will be associated with the “dynamical systems” research team and will work closely with the “probability-statistics” research team. To enable the transition to more sustainable agricultural practices, the development of new sensor technologies is essential. This is why these technologies are increasingly being deployed in experimental stations, fields, and livestock farms. These diverse and heterogeneous data sources enable both a better understanding of agricultural systems and the deployment of low-cost technologies for farmers. However, the specific characteristics of this data and the underlying stochastic processes prevent AI methods from producing accurate predictions. Combining learning methods with mechanistic models is becoming an increasingly important challenge that requires expert knowledge. You will contribute to this rapidly emerging field. Your work will focus on researching, studying, and implementing state‑of‑the‑art numerical methods for the inference and calibration of random dynamic models for data from sensors in livestock farming and agriculture.\n\nResponsibilities\n• developing methods for analysing data from sensors coupled with mechanistic models and studying their properties\n• analysing random dynamic models that include Markov processes, diffusion processes, hidden Markov processes, or deterministic models\n• establishing an inferential framework for estimating latent variables, such as breaks or mixture structures, using Bayesian, non‑parametric, or high‑dimensional statistical methods\n• interacting with researchers from different fields, including statistics and artificial intelligence, dynamic systems modelling, and agronomy\n• disseminating your work in the form of publications in journals in your discipline and more specialised disciplines, as well as in the form of packages (R, Python, Julia, etc.)\n\nYou will be expected to be able to implement some of the methods mentioned above and apply them to issues studied by the unit. Il sera attendu de pouvoir mettre en place quelques‑ones des méthodes citées ci‑dessus pour les appliquer à des problématiques étudiées par l’unité.\n\nNumber of offers available 1\n\n#J-18808-Ljbffr",
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  "job_posted_at_datetime_utc": "2026-03-11T00:00:00.000Z"
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