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secondments at UCL – University College London and at University of Leeds (UK). We are looking for you! Do you want to be trained to develop multi-level thrombosis risk prediction models by integrating insights
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dependencies, land concentration, soil degradation, and water pollution. Despite their lack of sustainability and resistance from some farmers, synthetic fertilizers continue to dominate agriculture worldwide
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biology to create next-generation disease models. In collaboration with ETH Zürich (Prof. C. Halin) and the University of Bern (Prof. C. Bourquin), this project aims to advance understanding of the human
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different methods such as modeling mass flows analysis (MFA), Life cycle analysis (LCA) and semi-quantitative methods for decision support for sustainable innovation. PhD Student in Safe and Sustainable Green
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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, adapt locally to users and environments, and remain computationally efficient. These models will be explored and benchmarked in software, with selected approaches translated into embedded prototypes
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) develops advanced optical technologies, including imaging, spectroscopy, and laser ablation methods. Our goal is to bridge these cutting-edge laser technologies with clinical practice, developing solutions
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established research group at the University of Zürich, focusing on the structural effects of posttranslational modifications within intrinsically disordered proteins. Our work combines biomolecular NMR with
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anthropology and social science to biostatistics and mathematical modelling as well as observational cohorts with biobanks. The Environmental Exposures and Health Unit (EEH) of EPH is focused on research related
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real