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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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candidate will hold a PhD in geosciences, applied machine learning, data assimilation, or applied mathematics. The selection will be based on the following scientific and technical criteria: Experience in
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Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No Offer Description The Machine Learning for Integrative Genomics team (https
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astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
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statistical inference, machine learning and population genetics. The expected outcomes include new computational tools for studying B cell evolution, insights into age dependent immune diversity and the
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to structured programming in C++ and Python - knowledge of linux / unix operating system - fluent knowledge of spoken and written English - fundamental knowlegde of machine learning (and statistics) - good level
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new thermoelectric materials using data science and machine learning methods applied to materials, based on expert-reviewed experimental data from the literature and public databases (notably
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, Applications of Deep Learning in Electromagnetics: Teaching Maxwell's equations to machines. Scitech Publishing, 2023. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR6164-DAVGON-024
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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning
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team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed