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Corteva’s Era trial dataset to parameterize crop cultivars, but also to infer future breeding requirements needed to offset expected adverse climate impacts. We will test the modeling framework in a reginal
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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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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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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