181 computer-programmer-"https:"-"https:"-"https:"-"https:"-"https:"-"CSIC" positions at NIST
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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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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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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
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RAP opportunity at National Institute of Standards and Technology NIST Autonomous experimentation and machine learning of material properties Location Material Measurement Laboratory, Materials Measurement Science Division opportunity location 50.64.31.C1094 Gaithersburg, MD NIST only...
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polymer networks, with well-defined chemistry and architecture, are needed to carry out quantitative measurements to establish design principles for programmable disentanglement or dissociation of network
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requires expertise in Computer Science, Statistics, or a similar field. Experience with machine learning, genetics, and/or bio-informatics is strongly preferred. The postdoc will work together and within a
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MultiPhysics Measurements and Modeling for Microelectronics at Microwave and mm-Wave Frequencies NIST only participates in the February and August reviews. Performance, security, and reliability of microelectronics systems are critical issues for the continued robust growth of the US economy....
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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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are essential for broad adoption of these methods, this postdoc would collaborate with a unique array of technology and informatics developers in the Genome in a Bottle Consortium to develop authoritative de novo
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correlations and prediction methods. The program will build on our existing efforts using Quantitative Structure-Property Relationship (QSPR) methodologies and modern machine learning methods (support vector