362 web-programmer-developer "https:" "https:" "https:" "https:" "https:" "https:" "University of Kent" positions at NIST
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research in high-impact science and engineering fields that utilize vapors, liquids, and aerosols. Our experimental scientists focus on developing fundamental measurements and novel methodologies that can
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Sorbent materials are candidates for many industrial and sustainable development applications, including carbon capture, hydrogen and methane storage, gas separation and purification, and catalysis. However
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, oxidation, and mechanical wear of chain scission in fibers are required to support the development of predictive models. This project seeks to utilize and develop novel chemical and mechanical techniques
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Tytus Dehinn Mui Mak tytus.mak@nist.gov 202.360.6799 Description In the past decade, the rapid pace of development in mass spectrometry technologies has accelerated the rise of metabolomics and resulted
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in biomanufacturing and personalized medicine. We are developing new electronics techniques that leverage the field effect, and optomechanical interferometric methods for the on-chip measurements
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exist for development of theory for and measurements of background and critical region thermal transport properties of such mixture systems. Proposals that integrate theoretical development with
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property data primarily intended for model development that investigate how the molecular size, molecular structure, and polarity of fuel constituents impacts their thermophysical properties. Measurements
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. Advisers name email phone Yamil Simon ysimon@nist.gov 301.975.8638 Description NIST has long developed and provided reference materials to assist others in making reliable measurements. The NIST Standard
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thomas.forbes@nist.gov 301.975.2111 Edward Ryan Sisco edward.sisco@nist.gov 301 975 2093 Description This opportunity focuses on developing and measuring the capabilities of ambient ionization mass spectrometry
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DeCost brian.decost@nist.gov 301.975.5160 Description Trustability and physical interpretability are critical requirements for the development of robust and sustainable machine learning systems needed