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University of California, San Francisco | San Francisco, California | United States | about 17 hours ago
radiographic exams and processes images on various archival systems. Professionally accountable for submitting high-quality images to PACS. Participates in performance improvement activities in the department
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: 87186BR The Nursing Imaging Director provides strategic, operational, and clinical leadership for all nursing functions across Diagnostic Imaging, Interventional Radiology, and associated ambulatory and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
the group of Dr. Eric Muir at the University of North Carolina at Chapel Hill in the Department of Radiology and the Biomedical Research Imaging Center. The successful candidate will develop and optimize
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See PhD theses | SCK CEN Selection process See PhD theses | SCK CEN Additional comments See PhD theses | SCK CEN Website for additional job details https://www.sckcen.be/en/thesis-and-internship-topics
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audiovisual corpus analysis tools - Interest in visual approaches to political processes - Ability to integrate into collective and multidisciplinary research teams Website for additional job details https
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. The primary research area will focus on applying advanced multimodal brain imaging, machine learning, and signal processing methods to evaluate patients with autism and related neurodevelopmental and genetic
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radar image processing, classification, multi-temporal analysis, and data fusion, using advanced automatic analysis methods such as deep neural networks and artificial intelligence is essential. Specific
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to establish and lead high-caliber research programs while actively contributing to the educational missions of both undergraduate and graduate programs within SLST. Application Procedure: To apply, please
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Social and Historical Sciences. For more information, please visit http://www.ucl.ac.uk/about UCL Mechanical Engineering UCL Mechanical Engineering has been pioneering the development of engineering
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by integrating sparse sampling strategies, neural network–based reconstruction, and a virtual imaging platform. The goal is to develop fast, robust, and clinically viable quantitative MRI methods