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team (https://research.pasteur.fr/en/team/machine-learning-for-integrative - genomics/) at Institut Pasteur, led by Laura Cantini, works at the interface of machine learning and biology (tools developed
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been revolutionized in recent years by machine learned interatomic potentials (MLIP), and questions that were impossible to tackle five years ago can now be addressed. The state-of-the-art approach
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imaging and machine learning. The main task of the successful candidate will be to help redefine certain traditional criteria of comparative anatomy used in archaeozoology and to establish new criteria
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Candidates must have expertise in at least two of the following areas: • Machine learning and its associated mathematical foundations • Embedded systems • Analog / mixed-signal design Website for additional
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at least two of the following areas: Machine learning and associated mathematical foundations Embedded systems Analog/mixed design [1] https://emergences.pepr-ia.fr [2] https://www.frontiersin.org/articles
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Ecole Polytechnique, in Palaiseau, France, and will consist of theoretical and numerical modellng. The thesis will consist of modeling turbulence using Machine Learning methods, in particular Physics
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use, utilizing innovative binary file analysis and deep learning to improve the security of computer systems. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR5104-MYRLAU-003