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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Posting Information Posting Information Department Biomedical Res Imaging Ctr-426001 Career Area Health Professional (Physician/Nurse/Other) Is this an internal only recruitment? No Posting Open
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the Department of Radiology will provide subspecialty interventional radiology services in a tertiary care academic medical center. This role includes delivering evidence based, patient centered image guided
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equivalencies: https://hr.uky.edu/employment/working-uk/equivalencies Required Related Experience No experience required. Required License/Registration/Certification Registered Nuclear Med Tech at time of hire
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• The Radiology Technical Assistant provides general support to the Imaging Department. This includes preparing the patient for the procedure/ exam, transporting the patient, assisting the technologist to transfer
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and overseeing the operation of a quality assurance and quality improvement process for the program. The Director reports directly to the Chair of the Department of General Dentistry but will work
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experience unless stated elsewhere in the job posting. Skills: Analytical Method, Analytical Problem Solving, Analytical Solutions, Communication, Computer Literacy, Computer Usage, Data Analysis, Data
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will close at 12:01 a.m. CT on the specified Closing Date (if designated). If you close the browser or exit your application prior to submitting, the application process will be saved as a draft. You
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advanced image processing and segmentation methods – computational and statistical analysis of large-scale neuronal activity datasets • Very good programming skills in Python (mandatory). • Teaching
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Graduation from a Radiologic Technologist Program accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT) Comprehensive understanding of essential Mammography imaging and
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cutting-edge research in areas such as pattern recognition, automation science, complex systems, AI for Science, robotics, machine learning, computer vision, natural language processing, biometrics, medical