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collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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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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motivated to acquire new skills. Candidates must be fluent in English and/or French with scientific writing skills. The doctoral contract will take place at the CRISMAT laboratory (https://crismat.cnrs.fr
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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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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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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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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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