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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Neural Net Deep-learning for Magnetic Resonance Image Reconstruction and Diagnosis Location
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analysis from laboratory and synchrotron x-ray diffraction, x-ray absorption spectroscopy for the quantification of chemical short range order, and automated microstructural image analysis. The simulation
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work enables precision calibration of imaging devices, so that sources of bias and uncertainty in image acquisition and reconstruction can be better identified, described, and sometimes corrected. The
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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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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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) 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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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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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