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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
to process the notes, EHR data as well as the images to advance the aims of the project. Mentors for the candidate will include Dr. Ashok Krishnamurthy and Dr. Matt Satusky from RENCI, Dr. Emily Pfaff from
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diabetes; genetics; infection and immunology; imaging and biomedical engineering; transplantation immunology; pharmaceutical science; physiology and women's health. We also have thriving research programmes
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Posting Information Posting Details Department Lampe Biomedical Engineering - 424601 Posting Open Date 07/22/2025 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar
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Number: JR89835 Position Summary The Integrated Biomedical Imaging Laboratory is seeking a Postdoctoral Research Associate interested in developing novel hybrid noninvasive imaging systems for studying
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Description Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in the Department of Radiology & Nuclear Medicine at the University
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Vision Group at the division of Signal processing and Biomedical Engineering develops intelligent systems for automatic image interpretation and perceptual scene understanding. Our research spans both
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will only be considered if the position has not yet been filled. Position description Professor Shiva Abbaszadeh in the Department of Electrical and Computer Engineering (ECE) at the University
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sensing, signalling and memory, critically influences the disease onset and progression1. The Iskratsch Group , at the School of Engineering and Materials Science, Queen Mary University of London is
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team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications. Communication: Strong and clear written and verbal communication skills
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potential applications in audio and music processing. Standard neural network training practices largely follow an open-loop paradigm, where the evolving state of the model typically does not influence