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on the influence of Alzheimer’s disease and aging on changes in cognitive functions in humans. The project combines cutting-edge technologies from genetics, proteomics and statistical modeling to understand
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: • Mathematical derivation, analysis, and comparison of models, methods, and simulation approaches. • Rapid prototyping of new ideas in custom code. • Implementation of new models, methods, and algorithms
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near-real-time forecast system for the Baltic Sea Generate high-resolution daily surface salinity maps for the Baltic Sea and validate them with available observational datasets Develop algorithms and
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persist after chemical insults, resulting in large-scale polarization of mutations in cancer genomes (Connor 2018, Aitken 2020, Anderson 2024), as well as how genetic background shapes the trajectory
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network
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Immunology and Computational Biology Reference number: 2025-0105 We are deploying advanced in vitro and in vivo model systems, genetic perturbations and single cell technologies with spatial readouts to study
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and simulation aspects across a wide range of fields - from biomechanics and geophysics to polymer-fluid coupling. Further areas of interest include numerical algorithms for high-dimensional problems
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project team on “Participatory Algorithmic Justice: A multi-sited ethnography to advance algorithmic justice through participatory design” (PARTIALJUSTICE) to examine issues of justice and participation in
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integration. Role: Apply ML to barley genomic data, emphasizing gene regulation and genetic variation Collaborate with geneticists, breeders, and industry partners to move research toward applications Explore
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that algorithmic parameters are tuned so that the over-approximation of the computed reachable set is small enough to verify a given specification. We will demonstrate our approach not only on ARCH benchmarks, but