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, biochemical, cell, and tissue biology method skills. Experience in using computational analysis (biostatistics, machine learning, data science, physics, or a related field). We value diversity and strongly
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, ATAC-seq, CUT&RUN, MERFISH, Visium), epigenomic data processing (chromatin accessibility, histone marks, enhancer mapping), multi-omics integration using Seurat, Signac, Harmony, ArchR or Scanpy, machine
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targets to treat anhedonia. Proposals that challenge prevailing assumptions, employ cutting-edge technologies, or integrate machine learning with neurobiological data are especially welcomed. Projects
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be
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written and spoken Willingness to engage in interdisciplinary collaboration and fieldwork Advantageous: Knowledge of bat ecology and species identification Experience with machine learning or automated
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-constrained machine-learning (ML) models in simulations of turbulent flows. You are expected to contribute to research and development in data-driven methodologies for turbulence modeling in LES (i.e., wall and
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array
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Are you interested in neuromorphic spintronic and can you contribute to the development of the project? Then the Department of Electrical and Computer Engineering invites you to apply for a one year
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individuals. iPSC “Village” systems and CRISPR perturbation to experimentally dissect and validate gene function in controlled, scalable cellular models. Advanced computational genomics, machine learning, and
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Postdoc (f/m/d) Leader of Junior Research Group "WEEE-Recycling" / Completed university studies (...
-hand experience in the application of machine learning, simulation and modelling concepts in resource technology # Proven track record of interdisciplinary collaboration along the value chain of raw