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scaled up to handle large numbers of samples in massively parallel, low-cost analysis systems. Before such systems can be realized, the electromagnetic response of biochemical samples must be understood in
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manufacturing methods such as additive manufacturing (AM) and post-process densification. The operative scale range for the void and phase microstructures of relevance extends from the micrometer scale down
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reviews. This research topic is not limited to the methods or techniques discussed below. We encourage interested researchers with diverse backgrounds and expertise in automation, mechanical
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NIST only participates in the February and August reviews. In situ characterization of advanced ceramic sintering processes The past decade has seen the development of several novel methods
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NIST only participates in the February and August reviews. This research opportunity centers on advancing experimental measurement methods to quantify complexes formed between charge-altering
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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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on improving standoff methods for measuring the flux of carbon dioxide from smokestacks and distributed area sources (5 km x 5 km) by advancing the applications of integrated path differential absorption LIDAR
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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
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the method impractical. We are exploring these state preparation ideas, as well as the metrology of the states produced and then once produced, the use of those states for high accuracy measurements. In
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collisional parameters, such as energy levels, radiative and autoionization rates, collisional cross sections, using the most advanced relativistic methods and codes. With the help of large-scale collisional