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to: stand or walk with, push and/or pull, objects up to or over 50 lbs.; regularly sit at a computer workstation for extended periods of time while performing tasks with repetitive motions (such as typing
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, with opportunities to lead analyses, develop new expertise and contribute to high-impact scientific publications, and must have: A PhD, or equivalent research experience, in computer sciences
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stimulation (TMS), and transcranial direct current stimulation (tDCS) through interdisciplinary collaborations. Behavioral measurements and physiological recordings, including facilities for computer-based
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, Electrical Engineering, or a related field. Strong foundations in machine learning, deep learning, computer vision, and at least one of 3D/4D vision, reinforcement learning, or robotics. Proficiency in Python
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
at the intersection of AI and cognition. Areas of research may include, but are not limited to, the effects of AI on learning, language processing, and other cognitive tasks; the development of AI-assisted technologies
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into the transmission X-ray imaging regime. The developed techniques will be validated on real data. As a candidate, you must have a strong background in machine learning, computational imaging, and/or computer vision
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and stress level is moderate to high. Noise level is quiet to moderate. Physical Activities Ability to work in front of a computer for extended periods of time. Occasionally required to move about the
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processed images and sequence of exposure and label appropriately. Review patient records for appropriateness of order. Prepare preliminary reports in collaboration with providers. Charge MR procedures and
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; ability to learn changing technologies related to grants and contracts management and federal guidelines and regulations; excellent computer skills, including proficiency with Microsoft Office (Word and
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computer-vision and foundation models, including self-supervised learning that learns from raw, unstructured data and adapts rapidly to high-stakes, domain-specific tasks (e.g., medical imaging, defect