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of the uncertainties encountered in the physical experiments. These computational methods have been applied to nanorheology in photopolymerization 3D printing [1], human breath research for forensic and clinical data
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learning with machine-controlled measurement tools for closed loop experiment design, execution, and analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods
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. The research includes experimental measurements on industrially-pertinent materials using a range of advanced methods (various modes of transmission electron microscopy, x-ray/neutron scattering, x-ray
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303.497.3480 Description Microbes are prolific (≈60% of Earth’s biomass) and exhibit substantial genetic diversity; however, laboratory methods for culturing these diverse microbes in vitro are severely limited
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requires expertise in Computer Science, Statistics, or a similar field. Experience with machine learning, genetics, and/or bio-informatics is strongly preferred. The postdoc will work together and within a
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RAP opportunity at National Institute of Standards and Technology NIST Computational Modeling of Progressive Structural Failure and Collapse Location Engineering Laboratory, Materials and
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. This research aims to develop the label-free chemical imaging method to investigate the microscopic structure and composition of new biomaterials and smart polymers via collaboration with material scientists and
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301.975.2461 Description Our goal is to develop and apply new computational (molecular simulation) and theoretical (statistical mechanics and thermodynamics) methods to study complex fluids, with an emphasis
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method that performs well across all types of microbial samples is very challenging. The difficulty increases considerably when quantifying microbial mixtures, which are increasingly relevant for many
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RAP opportunity at National Institute of Standards and Technology NIST Creating Computational Techniques to Investigate the Ensemble Structures of Flexible Proteins In Combination with Neutron