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algorithms to improve methods for peptide identification from raw mass spectral data. The use of orthogonal information such as multi-enzyme digestions, to verify the presence of a peptide using different
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; Electronic materials; Local structure; Nanometer scale; Pair-distribution function; Raman spectroscopy; Solid-state ionics; X-ray absorption spectroscopy;
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are interested in postdoc candidates with prior experience in any of the following areas: ceramic processing, nano-particle synthesis, colloidal chemistry (i.e., rheological testing, zeta-potential), advanced
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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and
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opportunities are available to advance the measurement of greenhouse-gas emissions from point sources such as power plants and distributed area sources such as landfills, farms and sequestration sites. Accurate
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on the presence and distribution of such strains. Many other high-impact studies are possible using techniques (both in situ and ex situ ) such as TEM, AFM, SEM, and X-ray diffraction on single crystals
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landscapes for promoter activity based on steady state population distributions and measures of fluctuations in individual cells. We have previously applied Langevin/Fokker Planck equations to predict rates
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, since every nanoparticle produced is not identical, it also utilizes new techniques like First Order Reversal Curves (FORC) to characterize the distributions in these properties. A variety of experimental
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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in directed evolution. This position