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computational tools to predict materials properties at the quantum level. In addition, electronic structure methods that go beyond the accuracy of DFT such as Quantum Monte Carlo, GW, and other advanced
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informatics developers in the public-private-academic Genome in a Bottle Consortium to develop methods to integrate short-, linked-, and long-read sequencing technologies to form benchmarks for somatic variant
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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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jessica.reiner@nist.gov 843.460.9894 Description The Analytical Chemistry Division has an ongoing program to improve the quality of analytical chemical measurements made in marine environmental research through
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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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for achieving recovery-based objectives, (3) computing the collapse risk of new and existing buildings and infrastructure systems, (3) developing improved nonlinear modeling capabilities to evaluate the response
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RAP opportunity at National Institute of Standards and Technology NIST Identifying Material Behavior from Measurements and Simulations in Advanced Mechanical Testing Location Material Measurement Laboratory, Materials Science and Engineering Division opportunity location 50.64.21.C0832...
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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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industries and research sectors. Our research group is interdisciplinary, drawing from diverse previous research experiences including wet-lab and computational work. Interested candidates are invited to reach
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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