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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
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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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-eddy simulation and direct numerical simulation of the phenomena. Topics of interest include algorithm development numerical combustion, scientific visualization, and data analysis. key words Buoyancy
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-deconvolution algorithms that can account for peak asymmetry due to imperfect shims; the use of spatially selective or multidimensional NMR methods; and the development of reference materials, especially for gas
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and then accessed by a team of experts. We are seeking candidates to address these challenges that range from algorithm development, simulation of reference data, algorithmic accuracy evaluations, design of
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and signal-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored for the particular nature of a given chemical or biochemical measurement problem by
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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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extensive measurement capabilities, CMOS circuit design work for foundry tape-out, and theoretical work developing new algorithms and architectures that leverage the low-energy, high-speed properties
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RAP opportunity at National Institute of Standards and Technology NIST Development of New Computational Methodologies for Molecular Simulation of Soft Materials Location Material Measurement
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, this project will employ emerging proteomics techniques (such as data-independent acquisition) and will be working alongside software and algorithm developers to ensure that these platforms can be used beyond