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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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Division Macroeconomic Forecasting and Data Science analyses and forecasts the Swiss and international economy and produces KOF’s short- and medium-term macroeconomic outlooks using macroeconometric models
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reinforcement learning for large language models (LLMs). Research directions include developing next-generation post-training algorithms, exploring diffusion-based approaches to reasoning with language models
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of adaptive radiation and associated key innovations in the evolution of freshwater diatoms. By integrating morphology, physiology, genomics, transcriptomics, and computational modeling, we aim to (i) determine
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field interactions Tuning of the CFD models with experimental results Artificial Neural Network training and development Scientific publications in journals and at conferences Supervision of students Your
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Simulation Framework The Computational Biology (CoBi) group, led by Prof. Dagmar Iber, develops data-driven, mechanistic models of biological systems using advanced imaging and computational tools. Our group
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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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. Empa is a research institution of the ETH Domain. Empa's Laboratory of Biomimetic Membranes and Textiles is a pioneer in physics-based modeling at multiple scales. We bridge the virtual to the real world
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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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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