220 evolution "https:" "https:" "https:" "https:" "https:" "https:" "BioData" uni jobs at NIST in United States
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on multicomponent systems. Current efforts focus on the development of flat-histogram methods, which have been applied to study a broad range of problems including the fluid-phase behavior of multicomponent systems
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on the development of nucleic acid-based standard materials and the application of emerging technologies to assist clinical testing efforts. key words Sequencing; Capillary electrophoresis; DNA; Clinical; Genotyping
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beam scanning electron microscope (FIB SEM) with x-ray spectroscopy and cryo capability, an environmental SEM, and a transmission electron microscope. This project will involve development of novel
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modeling is the parametrization of the force field. There are a large number of force fields in existence and significant efforts are spent on their development and improvement. However, to-date, development
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to this research is the development and application of real-time data analysis pipelines to process the vast, high-speed XRD datasets generated during AM processes. These pipelines will utilize
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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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proteases, and ion mobility adds layers of confidence to a given identification. Individuals with a background in mass spectrometry or software development are encouraged to apply. key words mass
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, materials modeling (including finite element simulations, and theory), and the development of a high-speed circuit to quantify fiber alignment in composites in real time. To develop this technique, a
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NIST only participates in the February and August reviews. This opportunity focuses on the development and implementation of liquid chromatography mass spectrometry methods for the quantitation
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materials research and development by orders of magnitude, and it is a core capability and focus area for the Data and AI-Driven Materials Science Group, MMSD, MML. This research opportunity centers