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Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
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RAP opportunity at National Institute of Standards and Technology NIST Characterization of Interfaces and Interphases in Polymeric Material Systems Location Engineering Laboratory, Materials and
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RAP opportunity at National Institute of Standards and Technology NIST Incorporating Theory and Domain Knowledge into the Machine Learning of Polymeric Systems Location Material Measurement
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is focused on facilitating reproducibility, improving detection limits, and expanding the measurement capabilities of microfluidic control and sensing technologies. Research opportunities are available
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controls the interactions of and between nanotubes dispersed for processing in liquid media. A key example of this is that liquid phase separation techniques rely on modulating these interactions using
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Integrated Photonic Interfaces to Free-space Volumes for Miniaturized Atomic and Molecular Optics Systems on a Chip NIST only participates in the February and August reviews. Postdoctoral research
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mechanical parts and assemblies (SolidWorks experience is preferable), experience with developing VI’s in LabVIEW, and experience with data and image processing (MATLAB experience preferable). [1] Huang, W
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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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on sources, detectors, and timing synchronization systems that can enable entanglement distribution over metropolitan and long-haul fiber links. Beyond entanglement sources and synchronization systems, we have