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alloys, carbon-based composites, and solid-state-biomolecule hybrid structures. Our data-driven development uses cheminformatics methodologies combined with machine learning methods to produce predictive
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RAP opportunity at National Institute of Standards and Technology NIST Data-Driven Technologies for Fluid Property Simulation Location Material Measurement Laboratory, Applied Chemicals and
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DFT, beyond-DFT, and experimental techniques. We are also interested in developing both forward and inverse machine learning models to accelerate and optimize the design processes. We work in close
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, and c) predicting new phenomena and discovering improved materials for applications. My efforts in this area use a variety of modeling approaches to answer questions on materials systems of interest
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to stochastic fluctuations using a data-driven landscape model. PNAS 109,19262-19267 Bhadriraju K, et al: “Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies.” Stem Cell
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driven flows; Combustion; Computational fluid dynamics; Fire modeling; Heat transfer; Large eddy simulation; Numerical combustion; Thermal radiation; Turbulent flows; Eligibility citizenship Open to U.S
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-based and data-driven prediction models are often impractical for operational use due to unrealistic assumptions, limited data availability, and prohibitive computational costs. To address
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RAP opportunity at National Institute of Standards and Technology NIST Coupling electronic structure methods, artificial intelligence, and data-driven approaches for next-generation quantum
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Laboratory. doi: https://doi.org/10.6028/NIST.TN.2178 . building control; intelligent agents; optimization; data analytics; machine learning; data-driven models; HVAC
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and data-driven models [2, 3]. Metric Identification: Identifying key quality metrics for various "digital objects" throughout the ICME development lifecycle. Uncertainty Quantification & Propagation