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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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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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measurement assurance to support the control and rational design of biological function. Through state of the art synthetic biology, automation, and machine learning, the Group creates living measurement
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learning, the Group creates living measurement systems, such as cells, engineered to sense and respond in programmed ways. Importantly, building these systems both requires and advances meaningful
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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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to acquire different kinds of images on large numbers of iPS cells in culture; machine learning algorithms and other image analysis strategies may be used to extract and test image features as predictors
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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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in the generation of an unprecedented quantity of data. This marks an inexorable shift of the field into the realm of “big data,” necessitating the development of novel machine learning approaches
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volume and quality that is consistent with the use of statistical methods; machine learning techniques for knowledge discovery; protein-protein interaction network analysis; novel algorithms for next