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atomistic simulation methods, such as molecular dynamics, density functional theory, and machine-learning force fields, to elucidate the deformation mechanisms activated by external stimuli. The candidate
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) | Working hours: 20.00 | Classification CBA: §48 VwGr. B1 lit. b (postdoc) Limited contract until: 31.03.2032 Job ID: 5115 Explore and teach at the University of Vienna, where over 7,500 brilliant minds
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machine learning (ML)) and emerging data types (electronic health records (EHR), biobanks and disease registries, and next-generation genomics including single-cell and spatial omics); communicates clearly
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Mattelaer, Christophe Ringeval). Research activities in include SM and BSM aspects of collider physics (LHC and future colliders, simulation tools, machine learning, effective field theories, amplitude
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Polytechnique de Paris. The group conducts research at the intersection of statistical learning, machine learning, and data science, with a strong focus on structured data, representation learning, and
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mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time-lapse data Proven programming expertise in
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genome-resolved multi-Omics methods, statistical/metabolic modeling, and machine learning. The postdoc will apply these approaches to generate a systems-level understanding of microbiomes including
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the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department of Informatics. You will be part of Visual Intelligence and the DSB group. For more information about the
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academic and general community. More information about the School of Graduate Studies is at: http://www.sgs.utoronto.ca/Pages/default.aspx Your opportunity: Working under the supervision of the Associate
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI