44 machine-learning "https:" "https:" "https:" "https:" "https:" "CNRS " positions at CNRS
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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stability analysis and control, machine learning, dimensionality reduction and high-performance computing. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UPR3346-NADMAA-159/Default.aspx
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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- 4 Additional Information Eligibility criteria • Experience in computer modeling and programming • Knowledge of associative learning at both the neurobiological and psychological levels • Experience
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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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manual gestures). The SyncoGest project (2025–2030) is an interdisciplinary project conducted jointly by computer scientists (Loria – University of Lorraine / Inria / CNRS), linguists (Praxiling – Paul
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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resources of CESAM, including its Machine Learning and Deep Learning hub, • close collaborations with ONERA. The successful candidate will work in a multidisciplinary environment bringing together researchers
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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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: The ideal candidate should have: * Knowledge of machine learning, especially neural networks or graph neural network or federated learning. * Strong mathematical and algorithmic background (optimization