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Field
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ternary and quaternary compositional spaces to provide experimental benchmarks for computational predictive models. The work will include planning and carrying out experiments, maintaining detailed
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computational approaches to problems in molecular and cellular biology. Research may involve large-scale public datasets, biological data harmonization, representation learning, predictive modeling, foundation
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, including the PBWT/GBWT family of haplotype algorithms for biobank-scale identity-by-descent (IBD) detection, the Med-BERT and CovRNN clinical foundation models for electronic health records, and the UDIP
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. Evaluating and improving modeled non-CO2 fire emissions during fires. Evaluating and improving modeled albedo changes after fires, including recovery and possible regrowth of vegetation. Making predictions
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. Our goal is to make mammalian cell engineering more predictive, scalable, and therapeutically actionable. Our lab works at the interface of synthetic biology, mammalian cell engineering, cell-based
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computing environments, workflow-management systems, containers, and/or software development practices. Experience with machine learning, predictive modeling, or artificial intelligence methods applied
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Integration group led by Dr. Jędrzej Szymański. Our group specializes in machine learning, multi-omics data integration, and the development of predictive models for plant gene regulation. We are part of
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models for in silico screening. Lastly, they will coordinate activities with partners in Scotland and Denmark. Mandatory requirements: Ph.D. completed within the last 7 years in fields such as Immunology
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the design of miniplates. Ad-hoc tuning of published mechanobiological models of bone healing will help create a robust model, able to predict long term surgical outcomes in patient specific cases. The results
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The main objective of the project is to develop novel, interpretable predictive models for response to immunotherapy in patients with advanced melanoma, based on the functional activity of gut fungi