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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientist
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seeking a Postdoctoral Research Associate to assist in the development, qualification, and deployment of Computational Fluid Dynamics (CFD) simulation codes, methods, and standard processes for thermal
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with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientists to develop machine learned parameterizations for vertical mixing in the ocean surface
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: solar magnetic field modelling, computational fluid dynamics, or solar observational data analysis. Working knowledge of at least one scientific computing environment (e.g. Python, Fortran, Matlab, C
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. Prior experience with computational modeling of fluid dynamics in experimental systems such as batteries, fuel cells and electrolyzers. Preferred Qualifications Strong research history in fields related
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coronary artery stents, and structural heart interventions. CORE FUNCTIONS Some of the main duties include, design, conduct, and analyze solid mechanical simulations and computational fluid dynamics studies
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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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Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks postdoctoral scientists or research scientists for research and development activities focused
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materials relevant to thermal energy storage, including conducting structural characterizations and studying their thermophysical properties. Proficiency in computational fluid dynamics (CFD) simulation
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investigate methods that eventually will automate crucial design steps. In addition, we are developing simulators (on various abstraction levels; using, e.g., Computational Fluid Dynamics) which enables us to