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mirrors based on Wolter optics, development of denoising and segmentation algorithms using machine learning [3], neutron energy-selective imaging, neutron phase grating imaging, polarized neutron imaging
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301.975.3507 Description Recent developments in Artificial Intelligence (AI) have allowed machine learning models to solve certain complex problems in natural language processing and other areas at large scales
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properties, as well as the absence of commonly accepted machine-readable data formats, requires tremendous human efforts to acquire the desired information from scientific papers, analyze the quality and
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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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parallel algorithms, execution of algorithms in the computer cloud, to delivering on-demand measurements over the Web. key words Image processing; Machine learning, Computer vision; Statistical methods
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Consortium led to the development of the first NIST RMs in this class, with widely-used benchmark germline variant calls for seven human cell lines [1]. Artificial intelligence and machine learning hold
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materials. The primary focus of this work is on mechanical characterization, microstructural analysis, and finite element analysis (FEA) and artificial intelligence (AI)/machine learning (ML) modeling
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functionalities, from crossbar-based machine learning to race-logic-based computing. Opportunities exist for experimental work in device fabrication and measurement using NIST’s state-of-the-art NanoFab and
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computer skills, including computer programming, are valued. Continued research is focused upon the emergence of complex event decision-making when there are concurrent and/or cascading risks involved
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facilities include a commercial laser powder bed fusion machine, a commercial laser directed energy deposition machine, and several unique open-architecture laser powder bed fusion metrology testbeds with