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the design and operation of next-generation thermal systems, developing strategies to model, optimize, predict, and autonomously control thermal systems behavior in complex systems. The candidate will
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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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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-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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data-model integration, leveraging the U.S. Department of Energy’s (DOE) Leadership-Class Computing Facilities to advance predictive understanding of complex environmental systems. Major Duties
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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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–experiment (ModEx) approach accelerated by artificial intelligence (AI) to advance predictive understanding of how plant–microbial–soil interactions vary across inundation and salinity gradients to shape
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AI to advance predictive understanding of how plant–microbial–soil interactions vary across inundation and salinity gradients to shape ecological, hydrological, and geomorphological responses to abrupt
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to post-process characterization. This environment enables the creation of high-fidelity digital twins and AI-ready datasets that support real-time monitoring, predictive modeling, and process optimization
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) with a particular focus on event forecasting, predictive maintenance, and corrective actions. The role would encompass working on forecasting, predictive, and text generation algorithms with Large