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systems using computer vision, quantitative image analysis, deep learning methods for detection, diagnosis, and quantitative analysis of abnormalities with multimodal data, including clinical and
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knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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* Experience in one or more of: AAV/vector biology, in vivo gene delivery, mouse models/surgery, single-cell/spatial transcriptomics, ATAC-seq, microscopy, quantitative imaging, computational analysis (R/Python
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well as transfers from other U-M campuses. Application Deadline Job openings are posted for a minimum of seven calendar days. The review and selection process may begin as early as the eighth day after posting
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. Experience working with medical images. Very good computer programming skills and physics background is essential. Desired Qualifications* Nuclear medicine imaging/dosimetry experience. Experience in image
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, operation of the scanner, data acquisition and analysis, image evaluation, and statistical analysis. The fellow will also be expected to prepare manuscripts and conference abstracts related to projects and
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environment in which people treat each other with respect and dignity, regardless of roles, responsibilities or differences. Providing support, direction and resources enabling us to accomplish the
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of Orthopedic Surgery at Regions Hospital in St. Paul, MN, within the broader NorthStar Trauma Network. The fellowship is a full-time, in-person, on-site role. This position centers on orthopedic trauma research
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photoelectrochemical ammonia synthesis funded by the Australian Research Council The successful candidate should have research experience in electrochemical technology and prototype design. They will have excellent oral