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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Neural Net Deep-learning for Magnetic Resonance Image Reconstruction and Diagnosis Location
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analysis from laboratory and synchrotron x-ray diffraction, x-ray absorption spectroscopy for the quantification of chemical short range order, and automated microstructural image analysis. The simulation
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work enables precision calibration of imaging devices, so that sources of bias and uncertainty in image acquisition and reconstruction can be better identified, described, and sometimes corrected. The
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software packages) is encouraged to improve reproducibility and to increase usage by the community. [1] https://doi.org/10.1039/C9SM01877H [2] https://doi.org/10.1063/1.5123683 [3] https://doi.org
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mechanism and the charge transport in thin dielectric films deposited on graphene using a non-contact microwave technique ( https://dx.doi.org/10.1021/acs.jpcb.9b11622 ) and monolayer graphene ( https
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Superalloys. J. Phase Equilib. Diffus. 43 , 931–952 (2022). https://doi.org/10.1007/s11669-022-01011-1 Keywords Calphad; Diffusion mechanisms; Multicomponent diffusion; Eligibility citizenship Open to
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: https://www.nist.gov/programs-projects/nanoscale-imaging-and-spectroscopy-ir-nir-vis-beyond-diffraction-limit ) and/or applying PTR on specific materials or nano systems with efforts balanced based
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Roxworthy BJ, Aksyuk VA: Nanomechanical motion transduction with a scalable localized gap plasmon architecture. Nature Communications 7: 13746, 2016. https://dx.doi.org/10.1038/ncomms13746 Brian J. Roxworthy