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advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data, with a focus on identifying critical transformation windows and assessing phase evolution kinetics
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that combine parallel architectures (i.e., GPUs or accelerator boards, clusters) and numerical algorithms suited to such architectures with the goal of improving the speed of convergence and the stability
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signals Innovate new in-line signal processing, measurement data analysis algorithms, and data visualization techniques. Innovate and test new instrument types and measurement methods, Work with commercial
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or algorithms for detection of the drug signal in complex mass spectral data and chemometrics for identification or classification of drug(s) is also of high interest.Through this opportunity, collaboration with
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particle dynamics (CFPD), a fluid-particulate coupled algorithm, to (i) quantify breath device operation (i.e., species deposition, complex fluid flow) [1], (ii) guide experimental breath species collection
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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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Sonmez Turan meltem.turan@nist.gov 301.975.4391 Description NIST standardized cryptographic algorithms are intended to be "bulletproof". That is, the computational complexity needed to break them is
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water sensor at the molecular level. Our measurement techniques and numerical models based on constrained regularization algorithms allow us to link these measurements with other techniques including
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resolution techniques are explored to achieve quantitative reconstruction of nanoscale structure images by developing novel DUV/EUV imaging optics and quantitative phase retrieval algorithms. A qualified
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areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research