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for the division, under the direction of the Imaging Manager. Minimum Qualifications 2-Year College Degree. A.R.R.T. Radiography registered with current ODH licensure. Previous interventional radiology experience is
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with a focus in advanced cardiac imaging, cardio-oncology, cardiac amyloid or cardiac intensive care medicine. Responsibilities will include patient care, research, and teaching (medical students
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, this segmentation may rely on artificial intelligence (AI) tools trained to automatically identify and delineate the main organs from MR images. This step will make the generation of attenuation maps faster, more
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https://university-communications.ecu.edu/ Advertising Department NEWS AND COMMUNICATIONS Division Chancellor Classification Title Specialist Working Title Public Communications Specialist III Number
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. Machine learning will assist in artifact correction, segmentation, and material classification. By combining experimental imaging, simulation, and data-driven interpretation, this approach will deliver high
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About Us The post will be based at St Thomas’ Hospital in central London in the School of Biomedical Engineering & Imaging Sciences at King’s College London: https://www.kcl.ac.uk/bmeis . There is
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Posting Details Position Details Title Division Chief of Neuroradiology Specific Title Assistant, Associate, (Full) Professor of Clinical Radiology & Imaging Sciences Appointment Type Open
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interventional radiologists, and more than 30 physician trainees. We are starting a Diagnostic Imaging Physics Residency program with 2 trainees. The Division provides diagnostic & nuclear physics support for UK
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Radiologist to join our expanding team in the Breast Imaging Division. This is an exciting opportunity for a dynamic physician to participate in a growing clinical program within an academic health system
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 5 hours ago
resolution mammography images. The model should be trained using a combination of weakly supervised learning, for images without local annotations, and fully supervised learning, for images with corresponding