16 machine-learning "https:" "https:" "https:" "https:" "https:" "CNRS " uni jobs at CNRS
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remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging. Recent advances in machine learning approaches provide a powerful
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, understanding and predicting their thermal conductivity from first principles calculations is very challenging. In this doctoral research project, we plan to use machine learning potentials to investigate
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to reduce the cost of clean hydrogen to $1/kg by 2031. The project proposes to address key scientific challenges by using molecular simulations (reactive force fields like ReaxFF and machine learning
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reconstruction - Estimation theory - computational methods and deep learning approaches. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7249-HERRIG-026/Default.aspx Work Location(s) Number
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time scales. To do this, we will build on a landscape picture of stochastic gene expression dynamics inferred from data using modern machine learning techniques. The results will inform us about how
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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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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
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of deep learning in many disciplines, particularly computer vision and image processing. Consequently, coding architectures based on deep learning and end-to-end optimization have been proposed [Ding 2021