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security. Expertise in data-driven modeling (ML for energy, forecasting, anomaly detection) and physics-informed learning. Real-time/HIL or embedded control experience (e.g., OPAL-RT, RTDS) and laboratory
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) for validating the NOSHAKE concept for forecasting induced seismicity as a result of fluid injection/extraction related to geothermal energy, geologic carbon storage and hydrogen storage. The position is related
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Argonne National Laboratory, a U.S. Department of Energy national laboratory located near Chicago, Illinois, has an opening for a highly motivated postdoctoral appointee in the Decision and
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simulations, and scenario forecasting—to evaluate dynamic energy-water futures and resilience strategies for diverse Idaho communities. Department Overview: Boise State University invites applications for a
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to model and simulate building energy systems. Use of machine learning techniques to forecast cooling demand. Proven ability to analyse complex information, including large datasets, and summarise
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bifurcation has already been observed. However, a precise understanding of the origin of those bifurcations is still lacking, preventing the forecast of such events. One possibility is that the triggering
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particular high-energy instrumentation, i.e. X- and gamma ray detectors and -optics. The division is currently active in the operation of instruments and data analysis from three satellites. The division also
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air laboratory to uncover how sub kilometer air–lake coupling influences convection, rainfall and heat budgets. The insights will advance coupled models and improve forecasts in vulnerable regions. As a
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, and advanced hydrologic modeling to projects in flood forecasting, remote sensing data assimilation, hydraulic modeling, environmental flows, and decision-support systems. The role involves developing
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Forecasting (WRF) and the HYbrid Single Particle Lagrangian Integration Transport (HYSPLIT) models. Experience in advanced multivariate techniques. Demonstrated problem-solving skills, a willingness to apply