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/parameterization building in ocean/atmosphere data or models double diffusion in geophysical flows. Candidates with interdisciplinary skill sets and/or capable of working on multiple topics are highly encouraged
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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
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and model vadose fluid transport in the deep vadose zone (10s to >100 m depth) in California's Central Valley. The research will focus on the use of geophysical tools to parameterize and validate
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on the coupled thermodynamic, kinetic, and transport processes taking place in the cell. In addition, the successful applicant may contribute to the design of experiments for parameterization of material-level
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| Atmospheric Physics group, Institute for Atmospheric and Climate Science (IAC) PhD Researcher in Cloud Modeling: Improvement of Microphysical Parameterizations based on Cloud Seeding Experiments 100%, Zurich