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between fundamental science and applications. Our interdisciplinary approach will be implemented by employing advanced theoretical models and sophisticated experimental methods. Key properties of atomic and
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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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PostDocs in the group to express and characterize enzymes, develop analytical assays, and engineer enzymes using cutting-edge experimental and computational tools. Within the framework of BiONiX, you will
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next fall, Marc Riembau as CERN/EPFL joint staff member. Research areas include quantum field theory, high-energy phenomenology, physics beyond the Standard Model and cosmology. Beside LPTP
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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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fundamental questions including: How can we best simulate Hermitian and non-Hermitian strongly correlated quantum systems and harness the power of both classical and quantum computing resources? How can we
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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