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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
world. Position Summary This Postdoctoral Research Associate will conduct advanced research in artificial intelligence, machine learning, computer vision, and medical image analysis. The position will contribute
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to analyze complex image and real-world datasets and identify clinically and psychologically relevant patterns Lead independent scientific research projects covering study design, data processing, statistical
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opportunities across UNMC. Required Qualifications • PhD in Neuroscience, Biology, Physiology, Pharmacology, or related discipline • Demonstrated experience with rodent stereotaxic surgery (must) • Strong
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nanoparticles or mRNA for in vivo imaging and to deliver RNA in vivo for disease treatment and cancer prevention. Additional Information: Required Education and Experience: PhD in Biophysics, chemistry
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. Education and scholarly development The postdoctoral associate will receive structured education in computer vision applications in medical imaging, machine learning, research methodology, responsible conduct
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with clinical research experience, or a PhD degree in biomedical engineering, medical physics, computational science, or a related field with experience in vascular imaging research. Experience in
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experience, or a PhD degree in biomedical engineering, medical physics, computational science, or a related field with experience in vascular imaging research. Experience in clinical or imaging-based research
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, Electrical Engineering, Applied Physics, Computer Science, Medical Imaging, or a related field. Strong expertise in ultrasound imaging, signal processing, computational imaging, medical image reconstruction
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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the development of novel approaches for medical imaging. The central aim is to understand how acoustic perturbations interact with spin relaxation in the presence of small-scale magnetic-field inhomogeneities. We