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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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RAP opportunity at National Institute of Standards and Technology NIST Engineered Quantum States of Light Location Physical Measurement Laboratory, Applied Physics Division opportunity location
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RAP opportunity at National Institute of Standards and Technology NIST Advanced computational modeling techniques to enable fast screening of RNA biopharmaceutical products Location Material
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RAP opportunity at National Institute of Standards and Technology NIST Mathematical Models for Characterizing Pluripotent Stem Cell Populations Location Material Measurement Laboratory
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@nist.gov 301.975.6256 Description Our project group is working to design and build a machine learning-driven autonomous system for genetic engineering of novel functionality into microbial systems
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RAP opportunity at National Institute of Standards and Technology NIST Efficient integration of sustainable fuels through the measurement and modeling of thermophysical properties Location
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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 Design, Characterization, and Modeling of Sequence Controlled Polymers Location Material Measurement Laboratory, Materials
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RAP opportunity at National Institute of Standards and Technology NIST Mathematical Modeling, Analysis, and Uncertainty Quantification Location Information Technology Laboratory, Applied and
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phases. This research will integrate a variety of modeling tools across multiple time and length scales to predict the microstructure evolution during the AM build process and post build thermal