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machine learning. We have in-house GPU servers for MD and machine learning, along with access to French national supercomputing resources. SAXS and SANS experiments will be conducted at ESRF. We are seeking
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 months ago
the IDPFold project (2025-2029) recently funded by the French National Research Agency (ANR). The main goal is to develop geometric deep learning models to study intrinsically disordered proteins (IDP). The PhD
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(which is often easier to create particularly in for multilingual processing typically by using machine translation) and further improving the model using preference data. Preference learning has gained
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selection, the marginal likelihood, and generalization. In International Conference on Machine Learning, pages 14223–14247. PMLR, 2022. [8] B. O. Muth ́en. Beyond SEM: General latent variable modeling
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. The PhD candidate will be in charge of i) the development of cutting-edge machine learning models correlating materials synthesis protocols with materials properties, and of ii) using such models in
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doctorale et l'établissement en ce qui concerne le taux d'encadrement de doctorants par un directeur de thèse. Le laboratoire IRIMAS propose ce sujet : HYBRID METAHEURISTICS AND MACHINE LEARNING METHODS
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the Institute of Applied Physics in Florence, Italy (IFAC) and to conferences in Europe to present scientific results. Knowledge of inverse methods, statistics or machine learning Knowledge of remote sensing from
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Mines Paris - PSL, Centre PERSEE | Sophia Antipolis, Provence Alpes Cote d Azur | France | about 2 months ago
-focused learning" or "End-to-end learning". For example, end-to-end machine learning (ML) models can be trained to minimize the downstream decisions regret or even directly learn a mapping from data to
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enrolled for PhD at the University Marie et Luis Pasteur, Besancon, at the CNRS laboratory FEMTO-ST and will work on leveraging advanced reinforcement and evolutionary learning techniques (e.g. co-variance
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computer science. Particular attention will be paid to scientific skills in the following areas: Machine learning, deep learning Explainable AI Mathematical modeling of images (variational, Bayesian, sparse