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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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regimes, and accurate geometry- and biochemistry-based trajectory analyses. However, detailed molecular dynamics simulations are often too time-consuming to become the basis of computational measurements
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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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., biomarkers, metabolites) must be evaluated using digital twins of breath device prototypes. Our digital twins are based on simulations using computational fluid dynamics (CFD) and computational fluid and
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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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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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outbreak of a novel E. coli strain O104:H4. However, this can be difficult using the existing platforms and software because of the constraints on sequencing (read length, depth), and informatics (e.g
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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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areas include the development of interpretable and trustworthy algorithms for Scientific Artificial Intelligence and active learning, integrating FAIR data management practices throughout the research
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RAP opportunity at National Institute of Standards and Technology NIST Algorithms for Compound Identification by Mass Spectrometry Location Material Measurement Laboratory, Biomolecular