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and carbon fluxes and quantitative modeling thereof. It also includes the isolation of new alkalophilic and hydrogenotrophic methanogens and acetogens, as well as Knallgas bacteria, the construction and
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, collaborating with researchers, policymakers, industry partners, and farmers who all work on translating complex data and modelling results into actionable insights. Key responsibilities Develop and apply
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modelling of cell–cell interactions, cell-state transitions, and tissue dynamics and multi-omics integration Applying ML approaches for biomarker discovery, predictive modelling, and development of diagnostic
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research in deep learning models for multi-sensor satellite data (e.g. SAR, SMAP) within a large international research project on AI-driven solutions for groundwater management. Expected start date and
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June 1, 2026, or soon thereafter. The position is within the research section Management and Modelling. The research section Management and Modelling develops methods and tools for herd management
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such as multi-level models, target trail emulation, mediation analysis and g-computation Supervision of bachelors’ and masters’ students You will report to Professor Christina C. Dahm. Your competences You
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tools or functional genomic information or OMICS to improve genomic prediction models. The persons hired will collaborate with industry partners, teach at undergraduate and graduate levels, and supervise
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, supported by advanced systems-level modelling and close collaboration with industrial and policy stakeholders. The successful candidate will contribute to the HyperCap research program focused on developing
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be used to prepare lamella samples for high resolution cryo-EM imaging and tomography. From AI assisted image analysis, 3D models for key proteins and biomolecular complexes will be fitted into 3D
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research project The project will address distinct metabolic pathways in prostate cancer progression. The study is based on an in vivo CRISPR mouse model for prostate cancer, introducing multiple mutations