307 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" positions at CNRS
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proposed by FLI and ALPAO will make it possible to tackle both the problem of speed, and that of spatial scaling in anticipation of the ELT. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre
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scientists working in diverse domains of fundamental and applied Physics. The postdoc will integrate the Condensed Matter group at CPHT. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7644
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Aiguier campus in the 9th arrondissement of Marseille. The LCB is composed of 100 staff members divided into 13 teams. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7283-DELLER-106
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modeling of polymeric, reinforced, and porous materials, with strong expertise in large deformations and numerical homogenization. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7649-JULDIA
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) in Paris. It will be co-supervised by Catherine AMIENS, from the LCC's 'Engineering of Metallic Nanoparticles' team (https://www.lcc-toulouse.fr/ en/engineering-of-metal-nanoparticles-team-l/ ) and
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, Organization and Interdisciplinary approach. Good skills in numerical methods and statistical analysis is required, very good English (written/spoken). Where to apply Website https://emploi.cnrs.fr/Offres
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astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
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conducted with Italian partners in Rome (Sapienza University, Catholic University of Rome, Gemelli Institute). Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR7252-VINCOU-004/Default.aspx
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statistical inference, machine learning and population genetics. The expected outcomes include new computational tools for studying B cell evolution, insights into age dependent immune diversity and the
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team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed