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NIST only participates in the February and August reviews. We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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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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Sonmez Turan meltem.turan@nist.gov 301.975.4391 Description NIST standardized cryptographic algorithms are intended to be "bulletproof". That is, the computational complexity needed to break them is
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to perceive latent correlations is critical to successfully integrating the vast amount of existing data, including biochemical pathways and enzymatic substrate specificities, in next-generation computational
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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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images. However, the current limitations of desktop computers in terms of memory, disk storage and computational power, and the lack of image processing algorithms for advanced parallel and distributed
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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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chromatography/mass spectrometry (LC-MS) measurements using principal component analysis, partial least squares, genetic algorithms, and other multivariate statistics. Current projects have accumulated a
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NIST only participates in the February and August reviews. The Alternative Computing Group at NIST has an ongoing program developing new metrologies to support emerging information processing
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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