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of results at conferences - interaction with team members and international collaborators The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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experience in scientific programming is a plus. • Experience in constructing Machine Learning potentials would be appreciated. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5254
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lie at the crossroads of multiple disciplines and involve expertise in optics, electronics, image and data processing (including machine learning), photophysics, chemistry and biology. The position is
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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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collaboration between the Exa-SofT and the Exa-DI projects and better support multi-linear algebra and tensor contractions in exascale CSE applications and Machine Learning. As part of the collaborative process
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of scientific and technical potential (PPST) and therefore, in accordance with regulations, requires your arrival to be authorized by the competent authority of the MESR. Where to apply Website https
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria The position requires a PhD in machine learning, NLP, causality, or a related discipline, with a strong command of deep
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a