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are being strengthened. Subject and project description This project is focused on the development of high-fidelity reacting flow models to investigate thermal runaway in battery cells, pack, modules, and
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, kinetic modelling, and computation fluid mechanics simulations. The new employee will have main task on the experimental part but also being involved in other parts as well. Work duties The main duties
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, have enabled the creation of proteins with near-atomic accuracy. Models such as RFdiffusion, LigandMPNN, and hallucination-based frameworks can now generate symmetric oligomers, cages, and backbones
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extensive testing with neuroimaging, biomarker measurements and neuropsychological testing. Our group applies sophisticated statistical analyses to these unique and state-of-the-art datasets to model
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of proteins with near-atomic accuracy. Models such as RFdiffusion, LigandMPNN, and hallucination-based frameworks can now generate symmetric oligomers, cages, and backbones while optimizing sequence properties
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genetic analyses and generalised linear mixed-effects models, and ideally also in the use of some mathematical models (e.g. Integral Projection Models) Experience in data processing, statistical analysis
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wide sense, with activities spanning from communication theory to channel measurements/modeling and implementation of advanced test beds. Your research will be performed mainly in the context of a large
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diabetes genetics and genomics. The methodological research may include but is not limited to statistical models using genetic data from family-based studies as well as -omics data for integrative
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. Teaching skills. Additional assessment criteria: Experience with research on stress signalling, metabolomics and phosphorylation cascades Experience with model crops, but also agriculturally relevant plant
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merited. Knowledge of energy markets Experience in research related to district heating and sector coupling Experience in energy system modelling Teaching experience Consideration will also be given to how