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. Skills in computational modelling or machine learning applied to brain signals are an asset. We are looking for a highly motivated, rigorous and curious researcher who is ready to invest themselves in a
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molecular dynamics simulations of modified nucleosomes - analyze the large data set obtained using various analysis tools, from visualization to automation using machine learning tools - perform QM/MM
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experience in scientific programming is a plus. • Experience in constructing Machine Learning potentials would be appreciated. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5254
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Polytechnique de Paris. The group conducts research at the intersection of statistical learning, machine learning, and data science, with a strong focus on structured data, representation learning, and
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The postdoctoral researcher will participate in the construction and
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silico identification of candidate developmental pathways explaining tradeoff variation. Contribute to advanced statistical analyses and interpretable machine learning approaches (in collaboration with
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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difficult to couple with basin simulators. Geochemical metamodels, particularly those based on machine learning, can significantly reduce computation times while maintaining physico-chemical consistency
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Institute of Molecular Mechanisms and Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. For more information about AMBER, visit: https