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their performance evaluated in terms of classification accuracy, computational speed, and overall usability. Required knowledge Deep learning (CNNs, Transformers) and computer vision Knowledge distillation for model
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, and MR spectroscopic imaging using machine learning; candidates with experience in these areas are encouraged to apply. PREFERRED QUALIFICATIONS: APPLICATION PROCEDURE: Apply online at https
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University of California, San Francisco | San Francisco, California | United States | about 5 hours ago
Imaging IT team architects, implements, and manages the computer systems and networks needed to support more than 700,000 imaging studies annually. Our team manages the full stack from network
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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: Monday – Friday, 8 a.m. – 5 p.m. Summary The Michael E. DeBakey Department of Surgery is seeking a Research Associate to implement and maintain the ATLAS (Applied sTatistics and machine Learning
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Science, Artificial Intelligence, Electrical Engineering, or a closely related field. Demonstrated expertise in Computer Vision, Image Processing, and Deep Learning methods. Experience with modern computer vision
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Computer Vision, Image Processing, and Deep Learning methods. Experience with modern computer vision frameworks and tools (e.g., OpenCV, PyTorch, TensorFlow). Strong commitment to excellence in teaching and
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facility working with the Director, Brian Armstrong, Ph.D. This position is for someone who enjoys working with people and helping scientists to achieve their imaging goals. You will be required to teach and
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: Experience in deep learning, machine learning and medical imaging processing Programming experience: Python, MATLAB, SPSS, Shell. Experience in working with Linux workstation. Excellent verbal and written
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.