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approaches for drug design by combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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inference, and machine learning, applied to longitudinal multi-omics data from pediatric cohorts spanning diverse socio-economic and geographical contexts. Our lab will be supported by two major grants
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Disease Modeling Lab (https://dbe.unibas.ch/en/research/data-driven-modelling-analysis/pediatric-disease-modeling-lab/) is seeking a Data and Computing Technician to build and maintain the data and
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care. Our work combines statistical and mechanistic mathematical modeling, causal inference, and machine learning, applied to longitudinal multi-omics data from pediatric cohorts spanning diverse socio
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and mechanistic mathematical modeling, causal inference, and machine learning, applied to longitudinal multi-omics data from pediatric cohorts spanning diverse socio-economic and geographical contexts