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assist others in the proper use of high quality computer operated microscope systems to include widefield, Confocal, and 2-photon microscope systems. As a successful candidate you will: · Train
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, 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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interpretation, equipment operation, and radiation safety protocols. Essential Functions Assist students in identifying anatomical structures and pathological findings on radiographic images Help student groups
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Computer Vision and Image Processing to lead the course, "Machines that see," which introduces students to core computer vision concepts, a vital area of AI. This role involves delivering expert instruction
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on glacier behaviour, including extreme events. For this work you should be familiar with image (pre-)processing techniques to obtain high quality quantitative data from time lapse imagery. This may include
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accessible through optical imaging. In this context, the team is developing a groundbreaking cellular-resolution imaging technology, the AO-RSO (Adaptive Optics Rolling-Slit Ophthalmoscope). This system
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
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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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with three or more years of research/relevant work experience. Preference will be given to those who have the following areas of expertise or equivalent: (a) image analysis and computer vision; (b
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Computer Applications: Microsoft Office Required Additional Knowledge, Skills and Abilities: Highly proficient in medical imaging toolboxes/software including FreeSurfer, FSL, AFNI, SPM, ANTs, ITK, c3d