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Field
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uncertainties and heterogeneities in material properties, inaccuracies and variability occurring during the production and assembly phase of the photonic chips and so on. The experimental work will be done in
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and machine-learning methods for rapid surrogate modeling, model calibration, state estimation, uncertainty quantification, and physics-informed prediction. Contribute to U.S. Department of Energy
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to SRC kinematics. Develop, test, document, and maintain scientific software for modeling, global fitting, uncertainty quantification, and data interpretation. Explore applications of the developed
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and uncertainty in downstream predictions; fine-tune ARCA for tasks including microbial root competence and crop-relevant outcomes, and iteratively improve the model using experimental Design-Build-Test
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credible claims of quantum advantage. Develop and apply physics-informed AI/ML and digital-twin capabilities to improve modeling, parameter inference, uncertainty assessment, and adaptive feedback between
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learning, as well as uncertainty-aware decision-making. The aim of these new algorithms is to develop policies that are robust, interpretable, and relevant for epidemic preparedness and response. As
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develop novel methodologies for representing and propagating uncertainty in temporally varying eruption source parameters, with a particular focus on time-varying mass eruption rate (MER) and plume height
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-time data acquisition and telemetry systems Familiarity with cloud computing platforms and edge deployment of ML models Experience with uncertainty quantification, sensitivity analysis, or robust
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uncertainty in downstream predictions; • fine-tune ARCA for tasks including microbial root competence and crop-relevant outcomes, and iteratively improve the model using experimental Design-Build-Test-Learn
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Contrails are aviation sector’s invisible climate threats with large uncertainties. You’ll investigate contrail formation pathways for various particles through lab experiments. Job description