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the biophysical processes as droplets wet membrane compartments in cells. Numerical simulations and theoretical models will be developed of the viscous interfacial fluid flow at cellular membranes alongside
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across four case studies: Spain, the Netherlands, Belgium, and Sweden. At the University of Oslo, we are seeking a researcher with statistical/physically-based modelling background and experience from
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sequencing data analysis (e.g., CAGE, ATAC-seq, ChIP-seq, or Hi-C) Familiarity with SUMOylation, transcription factors, or chromatin dynamics Expertise in statistical modeling for biological data. Knowledge
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); mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; and statistical machine learning. More about the position The position is
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digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domainspecific knowledge with data, laying the foundations of next
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Immunology to be based at the Department of Biosciences of the University of Oslo. This role contributes to the ERC-funded starting grant project “Fish-S.H.I.E.L.D.” starting on April 1st 2025, which aims
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Machine learning based modeling Energy systems modeling Fluent oral and written communication skills in English Desired qualifications: Good interpersonal and communication skills and show high level of
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on the understanding of how the biophysical processes as droplets wet membrane compartments in cells. Numerical simulations and theoretical models will be developed of the viscous interfacial fluid flow at cellular
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system diseases by combining gene engineering and generative machine learning modeling to design gene therapy constructs and validate the constructs in vitro and in vivo. The candidate will work in a
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our lab facilities, also incorporating theoretical models of complex flow. Fieldwork is planned in collaboration with a non-profit organization in Morocco, providing opportunities to test the concepts