19 learning-"https:" "https:" "https:" "https:" "https:" "https:" "CNRS" Postdoctoral positions
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19 Sep 2026 Job Information Organisation/Company CNRS Department Laboratoire de physique théorique et modèles statistiques Research Field Physics Chemistry » Computational chemistry Researcher
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scientific articles, presenting research results on conferences, and communicating with collaborators The Institute of Electronic, Microelectronic and Nanotechnology (UMR CNRS 8520 – https://www.iemn.fr/en
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heritage, within an academic–industry consortium involving GREYC–M2C–NORM3D–ROBORATIVE. Location: Image Team, GREYC UMR CNRS 6072 Supervisor: A. Elmoataz, Professor at the University of Caen Normandy
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6 Sep 2026 Job Information Organisation/Company CNRS Department Laboratoire "Atmosphères et Observations Spatiales" Research Field Mathematics History » History of science Researcher Profile First
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12 Sep 2026 Job Information Organisation/Company CNRS Department Institut de Biologie du Développement de Marseille Research Field Biological sciences Researcher Profile First Stage Researcher (R1
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20 Sep 2026 Job Information Organisation/Company CNRS Department Institut Photonique d'Analyse Non-destructive Européen des Matériaux Anciens Research Field Biological sciences Environmental science
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computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023-CLAMAR-001/Default.aspx Work
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Laboratory of Oceanography of Villefranche | Villefranche sur Mer, Provence Alpes Cote d Azur | France | 27 days ago
Contacts: Eva Ternon, Laboratory of Oceanography of Villefranche (LOV), [email protected] In the frame of the ERC consolidator UNIVOC project, the CNRS is seeking an enthusiastic research assistant
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: UFR SCIENCES ET TECHNIQUES Laboratory description: The Institute of Molecular Chemistry of the University of Burgundy (ICMUB – CNRS UMR 6302), affiliated with the Faculty of Science and Technology
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Recherche Scientifique (CNRS). The fellow will work on the project's “Decarbonization” pillar, with a focus on the analysis of the spatio-temporal dynamics of peri-urban forests, using LiDAR remote sensing