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Field
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optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and
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studies (e.g., EELS, EDS) to probe defect structures and dynamics Apply advanced image processing and analysis; develop AI/ML workflows for quantitative defect characterization Implement high-throughput and
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University. The ideal candidate will have a strong background in engineering—biomedical, electrical, or mechanical—with expertise in optics, imaging systems, or device development. Our research focuses
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the supervision of Dr. Merry Mani. The successful candidate will work on the development, validation, and translation of cutting-edge MRI techniques for imaging slow-flowing neurofluids and brain microstructure in
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
of uncertainty quantification to be used to inform the engineering process in a more systematic way. Science systems engineering focuses on ensuring alignment between the design and operation of an engineered
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image reconstruction methods, signal processing techniques, and data analysis pipelines for novel X-ray imaging modalities, including ghost imaging, quantum-enhanced imaging, and other correlation-based
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. Strong background in neuroimaging techniques (e.g., MRI, PET) and computational modeling. Proficiency in programming languages such as Python, MATLAB, or R. Experience with image processing tools and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 12 hours ago
for uterine fibroids using multimodal imaging and clinical information. The postdoctoral researcher will join a multidisciplinary research team consisting of computer scientists, neuroscientists, medical
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engineering-based research applicable to cardiovascular disease as part of an extramurally funded project. The successful applicant will create computational models from medical imaging data and run simulations