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-resolution or single-molecule imaging. ● Experience with machine learning/deep learning for bioimage analysis, including model application, validation and/or development. ● Experience with optical development
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deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification requirements PhD stipends are allocated
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days 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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 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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weighing up to 25 pounds; and sitting at a computer workstation for extended periods of time; and occasionally working at heights above four feet and/or in confined spaces. Also involves regular
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workstation for extended periods of time and performing tasks with repetitive motions (such as typing). Also involves some risk of injury from moving, lifting or positioning patients, equipment, or materials
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therapeutic targets. Develops and applies machine learning and statistical modeling approaches for biomarker discovery, classification, prediction, and patient stratification. Develops algorithms and
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management systems. Preferred Qualifications Experience supporting cardiovascular clinical research studies. Familiarity with image review processes, study protocol development, and sonographer training
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algorithms for computer vision, image processing, imaging, and/or physics-informed reconstruction. Scientific track record with publications at internationally renowned venues in machine learning. Solid
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research infrastructure. Apply advanced statistical, machine learning and data engineering methodologies to large-scale, longitudinal datasets, contributing to innovative melanoma and skin cancer research