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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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systems. Ths position requires a deep understanding of X-ray Absoprtion Spectroscopy and prior experience with methods of machine learning and artificial intelligence. A highly competitive candidate would
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is to measure to high accuracy the SI-traceable spectral energy distribution over the visible and near infrared wavelength range for a set of stars for use as flux standards for astronomy. In
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continues to push patterning to new limits. There are significant needs to understand how the components in these resists are distributed, and critically whether there is aggregation that could contribute
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NIST only participates in the February and August reviews. Additive manufacturing (AM) is a rapidly growing technology, but its commercial adaptation to ceramic-based materials lags behind
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methods for electronic-based manipulation and measurements of cells and their environment in well-controlled microsystems. This research opportunity focuses on the design, fabrication, and assessment of new
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NIST only participates in the February and August reviews. Metals-based additive manufacturing (AM) encompasses several different technologies that enable the controlled fusing, melting, and
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colonies with heterogeneous shapes, sizes, and levels of gene expression. This project focuses on systematically controlling inputs such as shape, size, and spatial distribution of colonies using cell
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the carbon dioxide system in seawater using methods such as total alkalinity, total dissolved inorganic carbon, and spectrophotometric pH, (2) evaluation of synthetic solutions to measure ion fitting
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-photon emitting state (Kartik and Zheng, 2017), and high-temperature organic superconducting state (Little, 1964). Recent progress in controlled modification of SWCNTs using ordered DNA wrapping ( Zheng et