172 evolution-"https:"-"https:"-"https:"-"https:"-"https:"-"BioData"-"BioData" positions at NIST in United States
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properties, facilitating the development of accurate standard tests and predictive methods, and their safe use at full-scale. key words Laminar burning velocity; Refrigerant flammability; Numerical flame
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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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@nist.gov 301.975.4127 Description This research is centered on the development and application of analytical methods to the characterization of nanomaterials. Opportunities exist to study the composition
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NIST only participates in the February and August reviews. Research on photovoltaics focuses on the development of new and improved device characterization methods for various cell technologies and
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are developing machine learning-driven autonomous metrology research systems, with the goal of accelerating the development of self-correcting photonic and quantum sensor networks. These systems combine machine
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data analysis techniques, instrument and sample environment development, and simulation methods to compare to experimental results. We are particularly interested in the development of two techniques
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characterization aims to address some of these fundamental photonic challenges and aid the development of novel nanophotonic technologies. These efforts are expected to enable realization of accessible hybrid
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are seeking researchers to contribute to the development and application of advanced measurement and automation techniques for exploring processing-structure-property-performance (PSPP) relationships in
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Development of magnetism-based future electronics is fueled by demand for large memory capacity and high data processing rates. New technologies such as hard drives with bit-patterned media and magnetic memory
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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position