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Field
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, physics-based reduced-order models. These novel models and parameterisations will ultimately be integrated into meso-scale weather models to aid in the design and performance prediction of future multi
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-assisted model calibration, or pattern detection in large observational datasets) to improve nutrient-cycling representation and reduce predictive uncertainty in models. Collaborate with an interdisciplinary
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system models and quantifying uncertainty in their predictions. Conduct fundamental research on the formulation of probabilistic system dynamics models, including knowledge integration, likelihood
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 12 hours ago
. Description: This project will develop a multimodal artificial intelligence framework to characterize and predict dynamic changes in forest structure, aboveground biomass, and carbon storage under hydroclimatic
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at Aalborg University, Aalborg campus, and become part of the Cracks in Composite Structures (CraCS) research group. The research group focuses on the predictive modeling, characterization, and quantitative
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Position Overview School / Campus / College: College of the Environment Organization: Applied Physics Lab Title: Postdoctoral Scholar in Physical Oceanography 4D-Var Modeling Position Details
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experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process signals to reaction behaviour, progressing from controlled
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, transdiagnostic cohort Integrating theory-driven models with data-driven analytic approaches to improve prediction of symptom trajectories Contributing to study-wide discussions of analytic strategy, model
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use numerical models to interpret and predict mechanical behavior. - Carry out a cross-analysis of experimental, microstructural, and numerical results. - Disseminate scientific results (publications
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analyze spatial and temporal datasets collected from UAS, satellite, ground-based sensors, and other sources. Integrate multi-source datasets and develop predictive models to support crop monitoring and