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develop, test, and implement new, physics/AI hybrid parameterizations of subgrid-scale processes in the Climate Modeling Alliance’s (CliMA’s) Earth system model. The focus will be on atmospheric processes
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implementation, testing and further development of new machine-learning parameterizations for subgrid atmospheric processes (convection and turbulence) and observations-based nudging tendencies in the Community
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represent? Why do over-parameterized models trained with simple optimizers generalize so well? We explore these questions through the lenses of statistical learning theory, optimization theory, and
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insights for folding pathways and kinetics. There is an exciting opportunity to advance the state-of-the-art in this field, which would have important and wide-ranging impacts. Training. PhD thesis in
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have a PhD in computer science, mathematics, physics, or related fields, with a passion for programming. A desire to contribute to the development of open-source software within the context of the agreed
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100%, Zurich, fixed-term The Atmospheric Physics group at the Institute for Atmospheric and Climate Science (IAC), ETH Zurich invites applications for a PhD position (3 to 4 years) focused
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controlling atmospheric composition, the detailed processes controlling this exchange are not well understood and highly parameterized in models. Long-term eddy flux observations, which are very limited world
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Ecology and Environmental Science are seeking a Staff Scientist with a PhD in Limnology or
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of highly accomplished faculty, postdocs, graduate students, and undergraduates, all of whom push the boundaries of their respective fields. The Department supports a PhD program in Geophysical Sciences
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Description This PhD project bridges computational neuroscience and machine learning to study the mechanisms of active forgetting—or unlearning—through the lens of both biological and artificial systems