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  "job_title": "Junior research scientist in multimodal data science",
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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 1463 ITAP Research Field Mathematics » Applied 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\nOffer Description\n\nYou will carry out your research activity within the Joint Research Unit UMR ITAP (Technologies and Methods for Tomorrow’s Agriculture), which brings together around 80 people, including 35 permanent staff members, organized into four teams. The Unit conducts research along the entire environmental metrology chain, covering all stages from measurements to understanding, decision-making and action. You will be assigned to the DéMo team (Decision and Agro-environmental Modelling), composed of about 10 permanent scientists with diverse expertise (data science, modelling, computer science, digital agriculture). The team’s research mainly focuses on the analysis and use of multimodal data from digital agriculture. It develops methods for processing spatial and temporal agricultural data to support operational decision-making for the management and characterization of agroecosystems. You will collaborate closely with researchers from the COMiC team (Optical sensors for Complex media), who design optical sensors based on spectral measurements and develop associated data analysis methods.\nThe exploitation of data from digital agriculture technologies represents a major challenge for supporting the transition of Agriculture. Collected across various spatial and temporal scales and drawn from multiple sources (sensors, remote sensing, connected devices, expert knowledge, crowdsourcing), these data offer unprecedented opportunities to extract valuable information and feed decision-support systems aimed at characterizing and managing agricultural systems.\nAchieving these objectives requires advanced data analysis methods capable of handling heterogeneous, noisy and incomplete data gathered under diverse agro-ecological conditions, while integrating expert knowledge and operational constraints.\nThrough a multidisciplinary approach, combining data science, spatio-temporal modelling and the integration of agronomic contexts, you will contribute to developing new methods tailored to technological evolutions and the challenges posed by the transition of Agriculture.\nIn this context, you will be responsible for designing, adapting and implementing data exploration methods that i) aggregate multimodal data, whether supervised or unsupervised, while integrating the specific context of digital agriculture, ii) jointly exploit the spatial and the temporal dimensions of structurally complex data, and iii) mobilize transfer-learning approaches to account for new pedo-climatic situations where filed data and expert knowledge are scarce.\nMobilizing these data for decision support requires developing original approaches adapted to their specificities, especially their multimodal characteristics, in a context where (i) high-quality, ground truth datasets are limited (tens of cases) compared to the number of observations available, (ii) model interpretability is essential to better understand and leverage the available observations and to validate model relevance and (iii) there is a high variability in pedo-climatic contexts, raising questions about the specialization and transferability of decision support models.\nWithin UMR ITAP, you will develop original methods for exploiting complex agricultural data to support decision-making and ensuring model interpretability ; design and implement the associated algorithms ; collaborate with researchers in data science and other disciplines (e.g. agronomy) ; monitor and contribute to the state of the art both at national and international levels in this rapidly evolving field.\nThe data science scientific community in Montpellier in highly dynamic (Université de Montpellier, INRIA, INRAE, CIRAD). You will actively participate in this ecosystem by establishing collaborations relevant to your research project. Within MathNum (Related scientific department if INRAE), you will interact particularly with scientists from UMR MISTEA (statistics, data science) and UMR TETIS (remote sensing, Machine Learning). You will also develop your own network of national and international collaborations, enhancing your research activities.You will participate in ongoing projects within the ITAP (for example TADAC Project, which focuses on developing",
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  "job_posted_at_datetime_utc": "2026-03-11T00:00:00.000Z"
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