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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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-informed neural networks (PINNs) and potentially generative adversarial networks (Pi-GANs). These models aim to predict cell fate and tumor development in CRC. The postdoc will collaborate with both
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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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be found on our lab website: https://www.derosierelab.com/ Salary and benefits: ~€2300 net per month for candidates with less than 2 years post-PhD experience; €2450+ net per month for candidates with
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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
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technical excellence at the service of education, research, and innovation. As part of the project led by the SOTERN team in connection with the CREACHLABS grant—which aims to reconstruct, at the network
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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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PyGIS • Networking ability (SNCF, private partners, CNRS research networks) • Good listening skills and ability to work in a team Additional Information Work Location(s) Number of offers available1Company
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postdoctoral fellowship at ENS Lyon in the field of machine learning. The position is part of the research project "Neural networks for homomorphic encryption", funded by Inria. Fully homomorphic encryption (FHE
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. LanguagesENGLISHLevelExcellent LanguagesFRENCHLevelExcellent Research FieldChemistry » Analytical chemistryYears of Research Experience1 - 4 Additional Information Benefits Salary: 2400 euros net Selection process Write a