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to perceive latent correlations is critical to successfully integrating the vast amount of existing data, including biochemical pathways and enzymatic substrate specificities, in next-generation computational
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, are attempting to expedite discovery by applying modern computational methods to identification and characterization of novel material systems. In this context, the NIST/TRC Group is building capabilities in
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, E.A. Lass, J.C. Heigel, Y. Idell, M.E. Williams, A.J. Allen, J.E. Guyer, L.E. Levine, Acta Mater., 139 (2017) 244-253. Additive manufacturing; Metals; Phase transformations; CALPHAD; DFT; Computational
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RAP opportunity at National Institute of Standards and Technology NIST Improving Sorting of Polyolefins for the Circular Economy Location Material Measurement Laboratory, Materials Science and Engineering Division opportunity location 50.64.21.C0942 Gaithersburg, MD NIST only participates in...
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RAP opportunity at National Institute of Standards and Technology NIST Chip-scale atomic magnetometers Location Physical Measurement Laboratory, Time and Frequency Division opportunity location 50.68.82.C0807 Boulder, CO NIST only participates in the February and August...
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NIST only participates in the February and August reviews. The need to reduce building energy consumption is well-established and has been the subject of research for many decades. More recent policy motivations have made building energy efficiency even more pressing. However, energy savings...
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; Electron microscopy; X-ray diffraction; X-ray computed tomography; Mechanical properties; Fatigue; Fracture; Modeling; Atom probe; Microstructure; Processing; Eligibility citizenship Open to U.S. citizens
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NIST only participates in the February and August reviews. The roll out of high-speed, low-latency communications networks is expected to drive economic growth through automation while creating new markets spaces that include self-driving cars and augmented reality. At the hardware level, the...
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RAP opportunity at National Institute of Standards and Technology NIST Incorporating Theory and Domain Knowledge into the Machine Learning of Polymeric Systems Location Material Measurement Laboratory, Materials Science and Engineering Division opportunity location 50.64.21.C0473...
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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