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. Research projects in the group have focused on the development and application of high-resolution measurement methods to study fundamental problems with broad industrial impact in areas such as the service
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. To study this problem, we are applying a microballistic-based measurement to understand the rate-dependent mechanical behavior of polymers from low impact speeds to ballistic speeds. We are
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well-structured form to the final industrial users. Recent advances in AI (Artificial Intelligence), specifically in NLP (Natural Language Processing) and ML (Machine Learning), are promising for
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measurements of the amount of each phase in the material, as well as how the phase fractions evolve during processing and deformation. Similarly, the grain orientations in most engineering materials have
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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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thermophysical properties influence the injection and combustion characteristics of the engines they are used in, which ultimately impacts their efficiency. The thermophysical properties of fluids group at NIST
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research will involve the development of beam-forming elements (including lithographic antennas or silicon micromachined corrugated feedhorns), superconducting elements for coherent processing of the
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complicated systems such as post-consumer resins, property changes from physical or chemical processing, the addition of other measurement modalities and/or using more sophisticated machine learning techniques
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) methodologies and modern machine learning methods (support vector machines, symbolic regression). Various aspects of data processing such as detection of erroneous data (outliers) and data balancing
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been applied to cells, tissues, and biomaterials. Our interest in further development is two-fold: (1) Numerical processing and analysis methods for extraction of actionable information from the rich