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of aqueous chemical models, incorporated into computer codes, for both pure and applied research that include industrial chemistry, chemical engineering, water treatment, hydrometallurgy, toxicology, medical
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to reliable manufacturing of the next generation computing devices. Computational imaging methods such as coherent diffractive imaging, Fourier ptychography, structured illumination techniques, and other super
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of the constraints on sequencing (read length, depth), and informatics (e.g., database composition, algorithm biases). Proposals should address these challenges with strategies to evaluate the metagenomic
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NIST only participates in the February and August reviews. This suite of projects seeks to advance the microbial metabolomics infrastructure through the development of new analytical methods, including data analysis and novel statistical approaches. We are particularly interested in...
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RAP opportunity at National Institute of Standards and Technology NIST Magnetic Resonance in Industrial Applications Location Physical Measurement Laboratory, Applied Physics Division opportunity location 50.68.62.C0945 Boulder, CO NIST only participates in the February and August...
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NIST only participates in the February and August reviews. The Applied Economics Office (AEO) at NIST works closely with the NIST Community Resilience Program (CRP) and external collaborators
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RAP opportunity at National Institute of Standards and Technology NIST Mathematical Modeling of Magnetic Systems Location Information Technology Laboratory, Applied and Computational Mathematics
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RAP opportunity at National Institute of Standards and Technology NIST Applied Optimization and Simulation Location Information Technology Laboratory, Applied and Computational Mathematics
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301.975.6050 Jan Obrzut jan.obrzut@nist.gov 301.975.6845 Description As part of a collaborative NIST-wide program involving structural characterization, modeling, and high-throughput microwave measurement, we
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measurements at 18-digit accuracy using an optical clock network. Nature 591, 564–569 (2021). https://doi.org/10.1038/s41586-021-03253-4 [2] Chave, A. D. (2019). A multitaper spectral estimator for time-series