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RAP opportunity at National Institute of Standards and Technology NIST Methods and Applications in Ab Initio Thermochemistry Location Material Measurement Laboratory, Chemical Sciences Division
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NIST only participates in the February and August reviews. This suite of projects seeks to advance the microbial metabolomics infrastructure through the development of new analytical methods, including data analysis and novel statistical approaches. We are particularly interested in...
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RAP opportunity at National Institute of Standards and Technology NIST Method Development and Best Practices for Atomic Clock Metrology Location Information Technology Laboratory, Statistical
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switched with single-flux-quantum (SFQ) pulses. Candidates interested in the logic aspects of this program should contact Sam Benz. Candidates interested in the magnetic memory aspects of this project should
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RAP opportunity at National Institute of Standards and Technology NIST Microfabricated Magnetic Sensors and Novel MRI/NMR Agents and Microdevices Location Physical Measurement Laboratory
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843.460.9894 Description The Analytical Chemistry Division has an ongoing program to improve the quality of analytical chemical measurements made in marine environmental research through analytical methods
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jessica.reiner@nist.gov 843.460.9894 Description The Analytical Chemistry Division has an ongoing program to improve the quality of analytical chemical measurements made in marine environmental research through
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jessica.reiner@nist.gov 843.460.9894 Description The Analytical Chemistry Division has an ongoing program to improve the quality of analytical chemical measurements made in marine environmental research through
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outbreak of a novel E. coli strain O104:H4. However, this can be difficult using the existing platforms and software because of the constraints on sequencing (read length, depth), and informatics (e.g
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correlations and prediction methods. The program will build on our existing efforts using Quantitative Structure-Property Relationship (QSPR) methodologies and modern machine learning methods (support vector