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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 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
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classification of scientific publications by their relevancy have been done at TRC. A successful applicant is expected to have a strong background in computer sciences, particularly in AI, NLP, and ML. No specific
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include the development of novel polymeric mechanical testing devices, novel adhesion blister testing devices, development of high-throughput screening devices, informatics, and data base development. key
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for critical applications that require qualification and certification—increasingly require that computational models and in-situ monitoring of such processes be experimentally validated under highly controlled
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memory, batteries, catalysts, flexible devices, alternate computing paradigms, and quantum phenomena. In order to take advantage of the promising properties of these heterogeneous systems, holistic study
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understanding of the physics of the QAHE necessary to design and develop new quantum resistance standards. Additional applications in quantum information science (QIS) can be envisioned for robust QAHE devices