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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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to identify metabolic response patterns and develop predictive models for personalized nutrition. Supervising master’s and/or doctoral students to a certain extent Possibility to engage in teaching at
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to predict thermal runaway on the cell level. The combustion and gas model developed on the cell level will then feed into the work to accurately predict thermal runaway on pack, module, and system levels
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The Department of Medical Biosciences is offering a postdoctoral scholarship within the project “Developing computational tools for large-scale human intracellular signaling models”. The scholarship
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longer-lasting charging strategy for Li-ion cells using two complementary approaches. (1) By testing commercial cells under various controllable stress factors and integrating lifetime prediction models
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on rodent-borne and vector-borne disease systems and their interaction with human mobility and societal connectivity. The project will further develop predictive modelling frameworks to reconstruct and
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regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty bounds and deriving
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Gaussian process regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty
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to predict thermal runaway on the cell level. The combustion and gas model developed on the cell level will then feed into the work to accurately predict thermal runaway on pack, module, and system levels
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are considered the largest source of uncertainty in climate predictions because it is complicated to accurately model the small-scale process (microphysics) inside clouds occurring in a range from meters to