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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 Atomistic Modeling of Phonons in Nanodiamonds and other Nanomaterials Location Material Measurement Laboratory, Applied
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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 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 Machine Learning Driven Autonomous Metrology System Location Physical Measurement Laboratory, Sensor Science Division
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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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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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performance. References Dols WS, Emmerich SJ, Polidoro BJ: Using Coupled Energy, Airflow and IAQ Software (TRNSYS/CONTAM) to Evaluate Building Ventilation Strategies. Building Services Engineering Research and
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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