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, multidisciplinary research team focused on advancing cancer care through cutting-edge computational and AI technologies. We develop innovative approaches that combine deep learning, computer vision, and
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ordenador a imagen hiperespectral/multimodal/3D y evaluación con métricas específicas. / - Usage of computer vision and hyperspectral, 3D and multimodal imaging techniques and evaluation with specific
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Vacancies Postdoc position on reinforcement learning on real-time image/video processing for medical robot Key takeaways In this role, you will be responsible of developing cutting-edge deep
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University of California, San Francisco | San Francisco, California | United States | about 17 hours ago
Enterprise Imaging IT team develops, implements, and manages the computer systems and networks needed to support more than 700,000 imaging studies annually. The Enterprise Imaging IT team manages a complex
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/equivalencies Required Related Experience 5 yrs Required License/Registration/Certification None Physical Requirements Sitting at computer workstation for extended periods. Repetitive motion. Ability to lift
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: image processing, machine learning, and patient records. Track record of development and implementation of novel machine learning algorithms in the healthcare setting or other spaces. Extensive experience
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knowledge and hands-on experience in machine learning, deep learning, or computational drug discovery Experience in processing and analyzing protein structure, chemical genomics, or single cell omics data
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: In decreasing priority order, experience in: • Model fitting and/or image reconstruction in astrophysics • Active Galactic Nuclei • Machine learning • Optical long baseline interferometry and data
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protocols, ITC will focus on the monitoring and response parts, building on many earlier projects revolving around the use of UAV/drones, computer vision and machine learning, change and damage detection, and
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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