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implement and train neural network architectures, including Physics-Informed Neural Networks (PINNs), in order to integrate physical constraints into the learning process and improve the identification and
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College London, through the Imperial- CNRS International Research Lab on Multiscale Metabolism. Where to apply Website https://emploi.cnrs.fr/Offres/CDD/UMR8199-HELDEG0-047/Default.aspx Requirements
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. The work will be primarily computational, focusing on the development of deep neural network model architectures and their training. It will involve extending the preliminary results we have already obtained
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research group of 6 permanent staff members, 3 PhD students and 2 non-permanent researchers within the Electronics team. He/she will also collaborate with other teams on campus, in particular the CIMAP
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experimental validation of mathematical and computational models linking individual microscopic dynamics, information propagation, and collective structures (norms, social networks, global performance
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has a strong societal impact ambition and requires particular care to protect intellectual property, while maintaining a collaborative research approach within a network involving other academic and
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) methods. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5253-MOUBEN-002/Candidater.aspx Requirements Research FieldChemistryEducation LevelPhD or equivalent Research FieldChemistryEducation
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of nanodevices and their multiple functionalities for bio-inspired computing. The team includes two permanent CNRS researchers, two Thales researchers, 4 post-docs, and 4 PhD students. Where to apply Website https
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validated at CPPM. In parallel, the candidate will improve data reconstruction algorithms by using artificial intelligence techniques (e.g. neural networks), to optimize the separation between signal and
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Required knowledge: - Degree: PhD, Engineering - Continental biogeochemistry - Environment and professional networks - Organization and functioning of higher education and public research - Language: English