20 machine-learning "https:" "https:" "https:" "https:" "https:" "CNRS " Postdoctoral research jobs at CNRS in France
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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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of nanodevices and their multiple functionalities for bio-inspired computing. The team includes two permanent CNRS researchers, two Thales researchers, 4 post-docs, and 4 PhD students. Where to apply Website https
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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning
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lie at the crossroads of multiple disciplines and involve expertise in optics, electronics, image and data processing (including machine learning), photophysics, chemistry and biology. The position is
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experience in scientific programming is a plus. • Experience in constructing Machine Learning potentials would be appreciated. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5254
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24 Jan 2026 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique de Grenoble Research Field Computer science Researcher Profile First Stage Researcher (R1) Application
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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molecular dynamics simulations of modified nucleosomes - analyze the large data set obtained using various analysis tools, from visualization to automation using machine learning tools - perform QM/MM
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collaboration between the Exa-SofT and the Exa-DI projects and better support multi-linear algebra and tensor contractions in exascale CSE applications and Machine Learning. As part of the collaborative process