62 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" "St" "St" uni jobs at CNRS
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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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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 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
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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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Ingénierie, Matériaux, ProcédésCountryFranceCityST MARTIN D HERESGeofield Contact City ST MARTIN D HERES Website http://simap.grenoble-inp.fr/ STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp
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/Default.aspx Work Location(s) Number of offers available1Company/InstituteLaboratoire des Sciences du Climat et de l'EnvironnementCountryFranceCityST AUBIN Contact City ST AUBIN Website http://www.lsce.ipsl.fr
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d'Electrochimie et de Physicochimie des Matériaux et des InterfacesCountryFranceCityST MARTIN D HERESGeofield Contact City ST MARTIN D HERES Website http://lepmi.grenoble-inp.fr STATUS: EXPIRED X (formerly Twitter
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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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City ST MARTIN D HERES Website https://liphy.univ-grenoble-alpes.fr STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail Pocket Viadeo Gmail Weibo Blogger Qzone
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. Wide-field coherent anti-Stokes Raman scattering microscopy using random illuminations. Nat. Photon. 17, 1097–1104 (2023). https://doi.org/10.1038/s41566-023-01294-x The project is part of an H2020/ERC