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or exceptionally well characterized property with known uncertainties. New biological “’omics” measurements, particularly sequencing-based methods, can produce 103 to 109 values from biological systems
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to reliable manufacturing of the next generation computing devices. Computational imaging methods such as coherent diffractive imaging, Fourier ptychography, structured illumination techniques, and other super
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photolithography methods. The self-assembly of the block copolymer is directed by a template patterned by conventional lithographic methods. The block copolymer structure within the pattern template can amplify the
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for significant improvement on the uncertainty in intensity. Current methods rely on dated methods and long traceability chains that result in uncertainty in intensity of over 20%. This is a fundamental limitation
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methods to temporally track distinct parent and progeny engineered nanomaterial populations. The development of methods to specifically follow the evolution of the smallest nanoparticle populations (< 10 nm
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Ravel bruce.ravel@nist.gov 631.344.3613 Description Develop methods of applying machine learning and artificial intelligence to synchrotron experimentation. This opportunity will be focused on operations
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://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
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of structural responses. References Wong KKF, Speicher MS: “Improved Method for the Calculation of Plastic Rotation of Moment-Resisting Framed Structures for Nonlinear Static and Dynamic Analysis”. Computational
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of the instrument fail or are attacked by an adversary. We would like to address the safety concerns by researching a metrology for establishing digital references [2], safety zones (boundaries), validation methods
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consideration will be made to candidates with experience in automation or machine learning. The postdoc will join a group which is focused on pioneering applications of modern machine learning methods, FAIR data