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), whole brain imaging, computational neuroscience, big data modeling, machine learning and artificial intelligence, will be prioritized. Newly recruited faculty will have the opportunity to be part of a
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implement cutting-edge AI solutions for real-time, image-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning
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Learning and Deep Learning libraries (e.g., Pytorch, Tensorflow) will be considered a significant advantage; Previous experience in image processing and\or computer vision will be considered an advantage
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
of Computer Science at the University of North Carolina at Chapel Hill seeks a talented, highly motivated postdoctoral researcher with a background in machine learning and/or biomedical image processing. The ACM Lab is
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objectives. For example, digital image processing tools, such as filtering or mathematical morphology, could be evaluated to extract structural elements of road edges from images. By combining spectral and/or
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fundamentals, including image processing, pattern recognition, and 3D geometry (share your GitHub profile) LanguagesENGLISHLevelExcellent Research FieldEngineering » Computer engineeringYears of Research
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of biomedical microscopy images. Train a machine learning (CNN) network to identify inflammation and damage patterns on H&E sections. Validate performance of algorithms on novel datasets. Properly version and
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems