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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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spatial and seasonal distributions of PFAS and identification of the key processes controlling their fate (dispersion, transformation, sediment retention). Contribution to modeling PFAS transport in
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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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permanent staff members, plus some 15 PhD candidates and 4 post-doc researchers. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5801-GERVIG1-053/Candidater.aspx Requirements Research
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melt than greenhouse gas emissions. - The high-resolution regional atmospheric chemistry-transport model CHIMERE will be used to understand the impact of terrain complexity and the spatial variability
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ExperienceNone Additional Information Eligibility criteria The postdoc should have a PhD degree in evolutionary biology, with expertise in bioinformatics, statistics, programming and/or modeling. Previous
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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