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Postdoctoral Research Associate (AC) - 25000683 Description : Postdoctoral Research Associate (AC) Psychology Department College of Education & Human Development Georgia State University
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Postdoctoral Research Associate (AC) - 25000398 Description : Postdoctoral Research Associate (AC) College of Education & Human Development Georgia State University Georgia State University’s
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of meteorological phenomena impacting the wind energy sector, in particular with respect to wind resources and wind conditions. For example, investigating how large-scale wind farm clusters may impact atmospheric and
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simulate wind and solar forecast uncertainties on pan-European level, leveraging latest machine learning weather forecast models Apply machine learning methods to forecast day-ahead and balancing market
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into the GIS TE tool e.g. availability, accessibility, weather window and extreme wave and wind metocean conditions; creating new datasets and incorporating them into the GIS TE tool; and increasing the techno
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. Washington Monthly Magazine ranks FIU among the top 20 public universities contributing to the public good. Postdoctoral Associate -The Wall of Wind (WOW) Experimental Facility The Wall of Wind (WOW
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Hypersonic Aerodynamics. The successful candidate will play a key role in developing and advancing new measurement systems for the departments Hypersonic blowdown wind tunnel, and conducting cutting-edge
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building indoor air quality (IAQ) and heating, ventilating, and air-conditioning (HVAC) system controls. The successful candidate will join the Indoor Environment Collaborative and Sustainable Building
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 5 hours ago
Joaquin Valley? What factors affect long-term trends of air quality conditions across the San Joaquin Valley? What opportunities exist for interdisciplinary collaboration and knowledge exchange with
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-carbon electric power systems, taking into account wake interactions between individual wind turbines. The project focus is on how to generate and utilize reduced-complexity predictive models for windfarm