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Field
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significant mathematical and computational background to learn and develop new AI methods. While demonstrated experience in computer vision and deep learning for pathology or other biomedical image analysis is
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MD degree. Must have relevant background in Biological/Biomedical Sciences or related Engineering field with a specific focus in cardiology, cardiovascular diseases, and cardiac imaging. Must have
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days 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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research areas include Biomedical applications, Communication systems and information theory, Digital systems and computer architecture, Microelectronics, Micro-electromechanical systems (MEMS), Nano
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models with a group of highly motivated scientists to employ interdisciplinary approaches such as retinal cell biology, functional genomics, imaging, and biochemistry. The Chen Lab has a well-recognized
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cell biology; cancer; cardiovascular; nutrition and diabetes; genetics; infection and immunology; imaging and biomedical engineering; transplantation immunology; pharmaceutical science; physiology and
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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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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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engineering, medical physics, or related fields, with proficiency in preclinical MRI, image data processing and analysis. Experience in handling and working with rodents is preferrable. Baylor College
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Research Associate specialising in statistical modelling and machine learning to join our multi-university multi-disciplinary team developing a groundbreaking technique based on autofluorescence (AF) imaging