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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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convection. The objective is to develop reduced but realistic models of cumulus life cycles that may be applied toward cumulus parameterization and/or machine-learning algorithms for predicting short-term
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-boundary MHD surrogates) for real-time predictions and control-oriented execution (<1 ms execution time). Advanced Control Synthesis: Synthesize and computationally test model-based (Model Predictive Control
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project. The position will provide core expertise in physics-based environmental and ocean modeling, with emphasis on numerical simulation and prediction of ocean conditions such as currents, tides, waves
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National Aeronautics and Space Administration (NASA) | Merritt Island, Florida | United States | 1 day ago
physiological responses. By incorporating these imaging-derived traits into genomic prediction workflows, the participant will explore how high-throughput phenotyping strengthens selection models. While
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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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oceanography, numerical modeling, acoustic propagation, and scientific computing. The postdoctoral researcher will develop, apply, evaluate, and integrate uncertainty modelling into the predictions
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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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is both experimentally realizable and accompanied by a digital thermal model suitable for real-time prediction and control. The two positions are closely coupled. One focuses on physics-driven thermal
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 1 day 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