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instrumentation, and custom software algorithms to improve methods for peptide identification from raw mass spectral data. The use of orthogonal information such as multi-enzyme digestions, to verify
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AI-Driven Experimental Materials Science Gaithersburg MD 50.60.02.C0708 NIST AI/ML on facility-scale electron microscopy data Gaithersburg MD 50.64.51.B8162 NIST Algorithms for Compound
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learning, Bayesian optimization, and similar methods. A key challenge is the integration of prior knowledge into the data analysis, including both device physics and material properties. We are primarily
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@nist.gov 240.688.7883 Paul Francis Salipante [email protected] 301 975 2820 Description Organ on a chip aims to be a more efficient way to gather pharmacokinetic data. To more accurately
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care technology. We are interested in various research activities related to computing and information technology. Specific areas of interest include intelligent medical devices; signal/data
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of phosphoryl transfer and catalytic turnover. Integrative modeling and machine learning have the promise of establishing new tools for combining computational and experimental data from HDX-MS and NMR
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microscopy, computational imaging, instrumentation for optical microscopy, data acquisition and automated stage/camera control, optical inspection metrology for semiconductor devices. Keywords Extreme
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Electronics; Microelectronics; Machine learning; Data informatics; Physics; Terahertz; Metrology; Eligibility citizenship Open to U.S. citizens level Open to Postdoctoral applicants Stipend Base Stipend
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microbial culture capabilities, as well as evaluating culture independent strategies (metagenomics, bioinformatics, synthetic biology) to determine microbial function from sequence data. Since this project
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interpreting the large amounts of data we now have access to. Qualified candidates will have a background in electron microscopy or a relevant branch of computer science. Keywords Scanning