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Postdoctoral Scholar to conduct qualitative modeling research in the Puget Sound region of Washington State, USA. Postdoctoral scholars are represented by UAW 4121 and are subject to the collective bargaining
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it fit to run on a supercomputer. You will apply your newly developed simulation tools within different research projects. This new position is embedded in the international research project LION 2 on
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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solutions and have the opporutnity to contribute to world-class research, we encourage you to apply! Opporuntities to Contribute Develops dynamic river-lake network models to track the geochemical and
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
models is highly desirable. Create a Job Match for Similar Jobs About The University of North Carolina at Chapel Hill Carolina rates among the nation’s great institutions of higher education, set
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Address Tucson, AZ USA Position Highlights The Lunar & Planetary Laboratory at the University of Arizona has an opening for a Postdoctoral Research Associate I in the field of modeling of planetary
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Posting Title Postdoctoral Researcher: Modeling and Stability Analysis for Modern Power Systems . Location CO - Golden . Position Type Postdoc (Fixed Term) . Hours Per Week 40 . Working at NREL
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focused on modeling multidirectional loading in anchors for floating offshore wind turbines. The role is part of the European project TAILWIND (tailwind-project.eu) and aims to integrate experimental data
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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productivity over time Fit structural equation models to test the effects of remotely sensed biodiversity (from hyperspectral and LiDAR) on forest productivity across three forest types (evergreen, deciduous