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simulations. Two complementary strategies will be employed: structure-based virtual screening (docking simulations + molecular dynamics) and ligand-based virtual screening (machine learning models). We have
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/ThreeBodyTB.jl), cluster expansion, classical potential development, and machine learning. In addition to work on specific problems, I work on developing new first principles-based modeling approaches, including
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and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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theory, Machine learning and multivariate statistics, Application in neuroscience, climate research, economics, ...). COBRA is a part of the Department of Complex Systems of the Institute
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, scalability, and effective performance across university use cases. Develops, trains, and fine-tunes machine learning models for a variety of university applications. Conducts experiments to evaluate model
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of computational methods that enable machines to perform tasks requiring perception, learning, reasoning, and decision-making. It encompasses core areas such as machine learning, data-driven modeling, intelligent
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analysis and processing: NumPy, Pandas, SciPy; - Machine learning/AI: Scikit-learn, TensorFlow, PyTorch (preferred); - Data visualization: Matplotlib, Seaborn, Plotly. LanguagesFRENCHLevelGood
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senior research position to work on projects related to computational analysis of mass spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational
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practical tools deployable in real-world clinical settings. This work is central to a multidisciplinary collaboration bringing together experts in neuroscience, machine learning, and clinical informatics
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, in close collaboration with the machine learning group at the Department of Informatics, both at University of Oslo. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs