201 associate-professor-computer-"https:"-"https:"-"https:"-"https:"-"UCL" positions at NIST
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. The Associate will utilize advanced measurement capabilities including LC-MS/MS, LC-HRMS, GCxGC-MS, ICP-OES, and NMR to develop methodologies for chemical contaminant profiling, or next-generation sequencing
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quality of dietary supplements and ultimately reduce public health risks that could potentially be associated with the use of these products. key words Dietary supplements; Gas chromatography; Liquid
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to measure the physical processes that contribute to the biological activation and inactivation of proteins and membrane proteins such as conformational changes, aggregation, and/or macromolecular association
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extensive internal and external collaborations, providing access to a full range of state-of-the-art materials characterization and computational modeling capabilities. The results will have broad
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via laboratory testbeds, numerous machining centers, and a nanoscale science center. Furthermore, NIST provides computational resources and has an interest group for AI that regularly meets, giving
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provides computational resources and has an interest group for AI that regularly meets, giving the successful applicant an opportunity to interact with a variety of NIST engineers and scientists. Smart
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the development of analytical methodologies, from both instrumentation and informatics standpoints, for the multifaceted and convoluted data that are obtained from complex biological, chemical, and forensic samples
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regimes, and accurate geometry- and biochemistry-based trajectory analyses. However, detailed molecular dynamics simulations are often too time-consuming to become the basis of computational measurements
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the potential of quantum states of light for advanced measurements and computation, integration in a chip-scale nanophotonic environment is required. In particular, the integration of single-photon sources with
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Information Technology Laboratory, Applied and Computational Mathematics Division NIST only participates in the February and August reviews. Machine Learning (ML) and artificial intelligence (AI