159 cloud-computing-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at NIST in United States
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NIST only participates in the February and August reviews. Microsensor systems are constructed from modular components that include device platforms, sensing materials, control and signal-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored...
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RAP opportunity at National Institute of Standards and Technology NIST Elucidating Factors Contributing to Sustainability and Efficacy of Nanomaterials in Complex Systems Location Material Measurement Laboratory, Materials Measurement Science Division opportunity location 50.64.31.B8272...
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NIST only participates in the February and August reviews. The Applied Systems team at the Center for AI Standards and Innovation leverages multidisciplinary methodologies to assess, evaluate, and measure AI systems in application and real-world settings to accelerate trustworthy innovation,...
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RAP opportunity at National Institute of Standards and Technology NIST Durability of Concrete Materials Containing Reactive Minerals in the Aggregate and Concrete Location Engineering Laboratory, Materials and Structural Systems Division opportunity location 50.73.11.C1032 Gaithersburg,...
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. These materials systems may have far-reaching applications, extending from neuromorphic computing to compact multiple-input multiple-output antennas. By achieving the aims of this project, this Associate will
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catalytic turnover. Integrative modeling and machine learning have the promise of establishing new tools for combining computational and experimental data from HDX-MS and NMR to explain the dynamics and
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NIST only participates in the February and August reviews. The chemical characterization of biomolecules and the measurement of their interactions at low copy numbers are critical for applications in biomanufacturing and personalized medicine. We are developing new electronics techniques that...
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-based and data-driven prediction models are often impractical for operational use due to unrealistic assumptions, limited data availability, and prohibitive computational costs. To address
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Division, where we develop instrumentation beyond the state of the art. Our research program offers a supportive, highly-multidisciplinary environment coupled with outstanding experimental resources
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group is working on a dual-track project to expand this class of materials, and the successful candidate will contribute to either the computational discovery or the experimental validation (or both