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Computer-based tools, including the NIST Alternatives for Resilient Communities model, or NIST ARC, are being developed to support community resilience planning [1, 2, 4]. The goal of NIST ARC is to
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digital models through an additive process. AM enables the rapid production of complex parts with minimal lead time, fewer constraints, and reduced assembly requirements. This makes it an attractive option
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augmented intelligent solutions that monitor, diagnose, and predict process performances to optimize production quality and yield. Proposals are welcome to develop augmented intelligent solutions
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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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-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored for the particular nature of a given chemical or biochemical measurement problem by optimizing and
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our understanding of the fundamental limitations of detectors and sources; development of new ways to package detectors, sources, and components optimized for few photon operation; and developing new
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NIST only participates in the February and August reviews. In many application areas, materials development increasingly involves manipulating the local atomic order to optimize properties
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. Chemical engineers constantly need reliable property data for process design development and optimization. This information is predominantly coming from scientific publications. Thousands of papers
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are designed, constructed and operated to provide optimal spaces for living and working. The industry needs tools, metrics, and processes to create buildings that are more than just static structures but living
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products. Assay development efforts should focus on the incorporation of a robust and optimized experimental design aimed at assessing the sources of variability, repeatability and reproducibility