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demonstrated background in scalable flood inundation modeling, Impact-based flood forecasting stormwater infrastructure design under uncertainty. We welcome applicants with recent PhDs and individuals seeking
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to test and compare strategies safely, calibrate models with real data, and support scenario-based decision-making. • Building data-driven models (e.g., forecasting, clustering/segmentation, learning-based
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algorithms (convex/nonconvex, stochastic/robust, MPC) for real-time dispatch, frequency regulation, and DER coordination. Integrate data-driven and physics-informed approaches for state estimation, forecasting
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modeling approaches-including machine learning (ML), hydrologic and energy systems simulations, and scenario forecasting-to evaluate dynamic energy-water futures and resilience strategies for diverse Idaho
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areas: Generative AI Agentic AI Graph Representation Learning and Modeling Foundation Models Large Language Models Multimodal Learning Forecasting Models Basic Qualifications A Ph.D. or equivalent degree
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago
) instruments into the GEOS model for its near-real-time aerosol forecast known as the GEOS Forward Processing (GEOS-FP) system as well as in its various reanalysis systems (e.g. Modern-Era Retrospective Analysis
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 months ago
system, hyperspectral infrared satellite radiance data have been used extensively and they have proved significant impact on reducing model forecast errors and constraining atmospheric states. This NPP
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of Engineering, at the University of Texas at Arlington, invites applications for a Post Doctoral Research Associate who will be working in the areas of improving forecasts of weather, climate and snowpack, and
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, AI-enhanced forecasting, optimization, or simulation-based analysis • Building and scaling cloud-based activity-based mobility analytics systems (AWS/Azure/GCP) for large multi-city datasets
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for physical and natural sciences, e.g., physics, chemistry, material science, weather forecast, biology, neuroscience, etc. Interestingly, these domains are characterized by the availability of huge amount