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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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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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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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301.975.2958 Description As of today, there is a plethora of cyber-physical instruments consisting of physical sensing (e.g., microscopy imaging) and cyber (digital) Artificial Intelligence (AI)-based
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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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already have expertise in at least some of the following areas: scanning microscopy, SQUID measurements, instrumentation for magnetic measurements, digital acquisition and control, optical and e-beam
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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