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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Leveraging Artificial Neural Networks for Enhancing GC and LC-MS Metabolomics Data
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Data Mining Methods Location Material Measurement Laboratory, Biosystems and Biomaterials Division opportunity location 50.64.41.B8332 Gaithersburg, MD NIST only participates in the February and August
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email phone Andrei Kazakov [email protected] 303.497.4898 Description Empirical correlations derived from existing experimental data have always played an important role in thermophysical
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the February and August reviews. Advisers name email phone Ala Bazyleva [email protected] 303 497 5981 Description Chemical engineers constantly need reliable property data for process design
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Keenan [email protected] 303.497.3665 Karl Francis Stupic [email protected] 303.497.4564 Description Neural net systems have been developed to process magnetic resonance imaging (MRI) data
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phone Edward Ryan Sisco [email protected] 301 975 2093 Description This opportunity focuses on the development of analytical methods and/or data processing techniques that could be used to
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analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration of prior physics knowledge into the data analysis
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, impairment indicators from other types of drugs are less facile to determine. This is due in part to a dearth of fundamental property data and a lack of measurement infrastructure for difficult to
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Carlo or molecular dynamics simulation, calculation of virial coefficients from intermolecular potentials, etc.) can be used to supply data where experiments are infeasible or unavailable, to test
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the possibility of accelerating 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