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particular, the research project will focus on inferring trajectories from spatial transcriptomics data modelling at the same time the cells evolution in gene expression and in space. Required skills : We
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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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this, the postdoc will use innovative modeling based on the coupling of (1) a meteorological model adapted to the fine spatial scales of these systems and explicitly simulating turbulent structures, and (2) a
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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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crosstalk with tumor cells, immune cells and the vasculature. We perform mostly in vivo studies, combining lineage tracing and depletion models, flow cytometry, confocal microscopy and unbiased approaches
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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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have a PhD in biology with relevant experience and interests. Experience with animal models would be an asset. LanguagesENGLISHLevelGood Research FieldBiological sciences » Biology Additional Information