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using advanced statistical methods. The goal is to improve our understanding of wave-driven turbulence processes, which are crucial for accurate climate modeling The Institute of Environmental Geosciences
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Information Eligibility criteria * Skills/knowledge: statistical physics, physical chemistry, machine learning * Expertise: molecular simulations, programming experience (Python, C++, Julia, C, etc.), Python
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analyses (bioinformatic and statistical) of environment and microbiome data, including amplicon sequencing libraries, metagenomes, and metatranscriptomes. • Publish (as lead and co-author) in leading
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cellular dynamics. Analyze large-scale transcriptomic and spatial dynamics datasets. Work in close collaboration with the team's biologists to test predictions from statistical models. Within the Polarity
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in Artificial Intelligence (Machine Learning and Statistics) at CentraleSupélec, · Joël Eymery, Head of the Nanostructures and Synchrotron Radiation Team at CEA Grenoble, · Jean-Sébastien
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and process behavioral and electrophysiological data • Model behavior based on diffusion models and make explicit link with neurophysiological data • Conduct detailled statistical analysis • Write
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of pedology and/or hydrology is a plus - Strong skills in ecological data statistical analysis Specific Requirements PhD degree of less than 3 years. LanguagesENGLISHLevelGood LanguagesFRENCHLevelGood
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of structural biology (biological membranes, membrane proteins). • General theoretical knowledge in the field of statistical physics, and knowledge of the principles of physical chemistry determining
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metabolomics, mainly meta-omics data analyses, statistics, use of databases…) Rigour, Scientific curiosity, Autonomy Communication, Writing and synthesis skills Additional skills Enzymology and enzymes
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required (e.g., Python, Mathematica, Matlab). Some knowledge of nonequilibrium statistical mechanics (Boltzmann and Vlasov equations) and magnetism (spin dynamics, Landau-Lifshitz-Gilbert equation) is