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Dynamic Atomistic Predictions of Crystalline, Crystal Defect and Liquid Metal Properties NIST only participates in the February and August reviews. Classical interatomic potentials provide a means
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teams. Unit URL https://imci.uidaho.edu/ Position Qualifications Required Experience Experience with statistical or predictive modeling as demonstrated by publications in the field Evidence of strong
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breed x system interactions. Including e.g. milk-based parameters according to other WPs, production system specific early prediction models for the control of endoparasites will be developed
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learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET chromatography simulation framework Simulation and analysis of batch and gradient elution
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NIST only participates in the February and August reviews. The fire modeling community is actively working to develop the tools needed to quantitatively predict material and product flammability
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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RAP opportunity at National Institute of Standards and Technology NIST Modeling Complex Microstructures Location Information Technology Laboratory, Applied and Computational Mathematics Division
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dataset generation technique to optimize the training of neural networks (NNs) for seismic data prediction. The use of neural networks to predict seismic velocity models has shown increasingly accurate and
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—these approaches can recover unmeasured near-wall structures, improve subgrid-scale modelling, and enhance predictive accuracy. Possible project directions include: 1. Reconstructing near-wall velocity fields from
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. Furthermore, a novel predictive algorithm of School-age neuropsychological outcome will be developed combining radiomic model of brain development, with qualitative neonatal MRI findings. Achievement