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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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conferences. • Contribute to the writing of scientific publications. Optional : • Design Machine Learning (ML) potentials. • Code in FORTRAN and PYTHON to improve the functionality of the global
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FieldBiological sciences » OtherEducation LevelPhD or equivalent Skills/Qualifications - The candidate must hold a PhD in microbial ecology or environmental microbial ecotoxicology. - Experience in the analysis
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on the ERC-funded project ‘Understanding the Consequences of Major Health Crises for Education: Learning from the COVID-19 Pandemic (LEARN)’. The LEARN project: Health crises, natural disasters, and violent
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applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
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silico identification of candidate developmental pathways explaining tradeoff variation. Contribute to advanced statistical analyses and interpretable machine learning approaches (in collaboration with
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University of Savoie Mont Blanc (USMB) that brings together expertise in machine learning and information fusion, as well as networks and systems. It develops methods for processing and managing data in
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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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and optimize their properties for neuromorphic computing through combined electrical and MOKE measurements, and train them to achieve artificial intelligence tasks. - Micromagnetic simulations - machine
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technologies (fiber-optic sensors, DIC), and computer science (machine learning tools) in collaboration with de department of Physics. The aim of the BriCE project is to develop a novel bridge monitoring