264 machine-learning "https:" "https:" "https:" "https:" "U.S" Postdoctoral positions at CNRS in France
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using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
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, MultiFab, TRI-Genotoul). The project is embedded in a dynamic regional and national collaborative ecosystem focused on organ-on-chip technologies and personalized medicine. Where to apply Website https
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research focuses on fundamental questions in automata theory on the one hand, and on algorithmic questions arising from concrete problems on the other. Where to apply Website https://emploi.cnrs.fr/Candidat
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team at the Laboratoire d'Informatique de Grenoble (LIG). GetAlp conducts research in NLP, machine learning, evaluation, and interpretability. The project will be supervised by Maxime Peyrard (CNRS
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for the rapid and effective treatment of severe traumatic wounds, particularly in civil or military emergency situations. This new system aims to combine strong adhesion in wet environments, on-demand UV
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(UCBL/CNRS/CNES/ArianeGroup). The laboratory has internationally recognized expertise (NASA, U.S. Defense Energy Center Support / DESC, etc.) in the fundamental chemistry of N-N interactions, including
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Machine/Deep learning and classification Knowledge of the Linux operating system for using a computing cluster Interest in transdisciplinarity and teamwork Autonomy and scientific rigor Website
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Website https://emploi.cnrs.fr/Candidat/Offre/UMR8628-MATMOR-005/Candidater.aspx Requirements Research FieldMathematicsEducation LevelPhD or equivalent Research FieldHistoryEducation LevelPhD or equivalent
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify