331 parallel-computing-numerical-methods-"Simons-Foundation" positions at NIST in United States
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guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
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-nitride (GaN, AlN, InN) material system. We are interested in nanowire growth techniques that include MBE, vapor transport, and catalyst methods. We are interested in a range of research topics, from
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NRC Programs at National Institute of Standards and Technology This page provides specific information related to the NRC Research and Fellowship Program at NIST. Use the navigation on the left
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characterization tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration
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NIST only participates in the February and August reviews. This program is designed to support the design, construction, and operation of high-performance sustainable buildings with good indoor
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parallel, low-cost analysis systems that do not rely on optical or aptamer-based labels. Before such systems can be realized, the electromagnetic response of biochemical samples must be understood in detail
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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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has an active effort in the development of electron microscopy methods for high spatial resolution materials characterization and has recently upgraded its aberration-corrected STEM with a high-speed
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methods to temporally track distinct parent and progeny engineered nanomaterial populations. The development of methods to specifically follow the evolution of the smallest nanoparticle populations (< 10 nm
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chemicals. While neutron and X-ray scattering methods are workhorse techniques for characterizing model formulations, the large number of components in many real products makes mapping the high-dimensional