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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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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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of Neuroinformatics (http://www.ini.uzh.ch/ ) invites applications for PhD student position to work at the intersection of Neuroscience and Electrical Engineering to study neural dynamics and plasticity mechanisms
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Science group at ETH Zürich welcomes applications for a fully funded Ph.D. position. We utilize state-of-the-art experimental infrastructure to investigate the dynamics of matter on its natural time scales
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have since helped halve global mortality, but this progress is threatened by rising insecticide resistance. We build quantitative, data-driven models to forecast the spread and impact of resistance
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how well reported dynamics of decline in rare plant species align with inferred trajectories of effective population size. Conversely, the project will also address whether habitat and species
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with ex situ experiments, demographic modelling or handling large datasets as well as holding a valid driver's license is a plus. Application / Contact Please upload your application via our online
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thoroughly analyzed with state-of-the-art theoretical models and published fully open access. The aim of our research is to advance the fundamental understanding of electrical insulation and current
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of information transmission, derive the theoretical models governing them and demonstrate their intriguing properties experimentally. The practical realizations of these topological metamaterials will be based
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nanoparticle systems. Investigate model particles such as liposomes, mesoporous silica and silver nanoparticles. Investigate RNA-LNP formulations for next-generation gene therapeutics, examining how lipid