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the polymers, devise strategies to tether them to EVs, and test the obtained hybrid delivery platforms for functional cargo delivery of model- and pharmaceutically relevant cargo in vitro. Additionally, you will
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handling, enabling first-time-right manufacturing. The predictive quality of these tools relies on accurate constitutive models that describe the behavior of the molten material during forming. With
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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Infrastructure? No Offer Description Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description Positive tipping points
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redesigning the drivetrain of bicycles and handcycles. As a PhD candidate, you will use musculoskeletal modeling and experimental testing to explore how muscles and limbs should move to deliver maximal power
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networks are built on the same geometric foundation, namely Euclidean geometry. This choice however leads to fundamental limitations that cannot be overcome with bigger models and larger datasets. A critical
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unprecedented real-time visualisation of these processes, yet systematic investigation across diverse rock types and integration with predictive models remains lacking. In this PhD study, you will be performing
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where trustworthiness is essential. Today’s explainability methods often do not provide meaningful decision support and, in some cases, can even leak sensitive training data. Models are especially
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–specific ways, and their downstream biological effects remain poorly understood. This project tackles this fundamental gap — by building computational models that simulate what goes wrong in the brain, one