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17 Sep 2025 Job Information Organisation/Company CNRS Department Laboratoire national des champs magnétiques intenses Research Field Engineering Chemistry Physics Researcher Profile Recognised Researcher (R2) Country France Application Deadline 7 Oct 2025 - 23:59 (UTC) Type of...
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This position concerns a 2 years contract for the development of experiments on complex biomolecular
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(funded by the French National Research Agency, ANR JCJC) aims to develop a new theoretical and numerical framework to describe correlated electron–positron dynamics in atoms, and to identify experimentally
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symmetry and excitation processes on vibrational spectra, as well as participate in the development of methods integrating new laser technology. The candidate will work within the framework of the ANR
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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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potential. This post-doctoral project aims to develop a predictive model of tumor growth based on artificial intelligence (AI) approaches capable of integrating data from morphological MRI and MRS
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on the development of deep learning methods for reconstruction and physics analysis of the ATLAS experiment data. The successful candidate will develop innovative analysis methods for the reconstruction or the physics
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of understanding cirrus clouds in climate studies. We are seeking postdoctoral researchers to contribute to our mission of advancing knowledge on how aerosols influence cirrus formation and evolution under both
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motivated Postdoctoral Researcher to join our team for a 18 months position based at Femto-ST in Besançon. This role offers a unique opportunity to contribute to pioneering research and development in
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop