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trigger reconstruction architectures for future particle collider experiments, based on deep learning models distributed across multiple hardware processing stages. The mission of this position, based
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processes that enable cells to move through a biological matrix in the absence of adhesion, by exploiting the topography of their environment. The consortium brings together two teams of physicists
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project on the release of hormones, neuropeptides, and neurotransmitters by exocytosis, which is mediated by a vesicular fusion process whose mechanisms are well established. However, the mechanisms
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, involving expertise in optics, electronics, image and data processing, chemistry, and biology. With the support of several European funding programs, the team is building a data science and machine learning
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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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systems. Laboratory simulations under astrophysically relevant conditions will provide key insights into asymmetric photochemistry and post-irradiation alteration processes. Where to apply Website https
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models is a plus. ● Familiarity with GWAS, population genetics, or bioinformatics pipelines are a plus. ● Experience with the processing of genomic biological data (whole exome or genome sequencing) is a
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micro-organisms (bacteria, fungi, algae, etc.), whose heterotrophic activity contributes to the chemistry of organic carbon. The aim here is to explore autotrophic processes (carbon fixation by
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the framework of the ANR EmergeNS whose aim is to understand, through mathematical and computer models, the role that autocatalysis, multistability and spatial heterogeneity may have played in the emergence
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is important to control their operating temperature and improve the dissipation of the heat generated during operation. The study is divided into 3 tasks : • task 1: the first task is devoted to a