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computer vision and machine learning. Our computational methods development has three primary goals. The first goal is continued support of expert-driven biomolecular structure determination by NMR, with
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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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through the application of machine learning, intelligent optimization techniques, automated fault detections and diagnostics, automated commissioning tools, and management of local generation and flexible
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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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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
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are not sufficiently accurate, or the methods are too expensive to accurately model sufficiently large systems. As a result, these computational problems are ideal for developing machine-learned potentials
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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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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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; Microelectronics; Machine learning; Data informatics; Physics; Terahertz; Metrology; Chemistry; Materials engineering;
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practices for benchmarking germline small-variant calls in human genomes. Nature Biotechnology 2019, 37, 555. Genomics; Bioinformatics; DNA sequencing; De novo assembly; Machine learning; Reference materials