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of LC-MS patterns of biological fluids. Previous knowledge and experience of a high-level programming language--preferably C++, Python, or Java and of pattern recognition techniques--to draw statistically
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. 84–94. Neuromorphic computing; artificial intelligence; memristors; RRAM; CMOS; machine learning; cognitive computing; phase change memory; superconducting circuits; magnetic tunnel junctions; race
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concepts. This research opportunity is concerned with expanding the knowledge base for cryptography in a quantum era. The focus will be on mathematics and theory. Particular topics of interest include: post
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standards and particularly to full classes of materials. Techniques such as multi-scale computational modeling are needed to distill the current knowledge of hydrogen embrittlement mechanisms, from
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volume and quality that is consistent with the use of statistical methods; machine learning techniques for knowledge discovery; protein-protein interaction network analysis; novel algorithms for next
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) assessment of laboratory quantum systems. The candidate will develop custom optimization algorithms that combine ML with domain knowledge to establish fully automated control protocol. The proposed protocols
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505 K to 933 K, the current benchmarks for relative JNT are 26 ppm to 35 ppm. Results in this temperature range are already directly impacting our knowledge of the platinum reference function that is
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remediation, and infrastructure corrosion. Emergent behavior in these communities is mediated by subtle chemical and spatial cues; however, little knowledge exists to model or control that behavior due
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nanometers, and we wish to measure that displacement to a few ppm (i.e., 50 femtometer). Determining the change in the gravitational field requires knowledge of position and movement of large cylindrical
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. The knowledge of viscosity is essential for designing chemical reactors, pipelines, pumps, refrigeration equipment, etc. Several experimental methods exist and are widely used for measuring viscosity; however