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photolithography methods. The self-assembly of the block copolymer is directed by a template patterned by conventional lithographic methods. The block copolymer structure within the pattern template can amplify the
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Description We work with scientists in other NIST laboratories to develop tools for computer simulation and analysis of magnetic systems at the nanometer scale. Model verification is achieved by comparison
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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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RAP opportunity at National Institute of Standards and Technology NIST Lightweight Cryptography for Resource Constrained Applications Location Information Technology Laboratory, Computer
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NIST only participates in the February and August reviews. Research focuses on hydrodynamic simulations and development of probabilistic methods for (1) characterization of hurricane storm surge and
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methods such as formal grammars, logic-based semantic representations, and knowledge graphs to improve the representation of knowledge, the evaluation of AI systems, and classification of documents and
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relate to and inform uncertainty estimates. Our current research addresses such problems by combining physics with tools from applied analysis, probability theory, asymptotics, optimization, and numerical
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processing of tables, extraction of property data from plots, analysis of paper content and extraction of metadata (substances, description of their samples, experimental methods, uncertainties, etc
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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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, Siamak Sattar, David Butry, Steven McCabe, “A Predictive Modeling Approach to Estimating Seismic Retrofit Costs”, Earthquake Spectra, (2019), 36 (2), 579-598. Honglan Huang H., Henry Burton, Siamak Sattar