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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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the coupled physical process of GCS, such that we can efficiently forecast the spatial-temporal patterns of the subsurface response variables, e.g., pressure, saturation, minerals etc.; (2) integrate
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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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for seasonal prediction using hybrid physics-machine learning models in R&D item Research on Seasonal Meteorological and Oceanographic Forecast Simulator under Development of Integrated Simulation Platform
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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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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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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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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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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 | 2 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