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Vice President of Philanthropy Brand Communications leads brand communications strategy and initiatives for philanthropic audiences, supporting the Division of Philanthropy?s revenue and engagement goals
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Location ESTEC, Noordwijk, Netherlands Our team and mission The Future Engineering Division (in support of the program directorates) is responsible for developing and providing the right Engineering
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detection, classification, and segmentation using real-world datasets. ii. Design and implement software modules to integrate the models into a working system prototype. iii. Perform data annotation. iv
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. Machine learning will assist in artifact correction, segmentation, and material classification. By combining experimental imaging, simulation, and data-driven interpretation, this approach will deliver high
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https://university-communications.ecu.edu/ Advertising Department NEWS AND COMMUNICATIONS Division Chancellor Classification Title Specialist Working Title Public Communications Specialist III Number
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the Interventional Radiology Division, including but not limited to the following: All single plane and bi-plane digital angiographic suites, including fluoroscopic, digital angio, dyna CT and 3D image reconstruction
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University of California, San Francisco | San Francisco, California | United States | about 14 hours ago
Cardiac Imaging; General Cardiology, Interventional Cardiology; CardioOncology and Immunology; and Prevention. The Division runs several clinical practices in multiple sites, conducts basic and clinical
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the University of Amsterdam (UvA) as an Assistant Professor in AI and Medical Image Analysis within the Quantitative Healthcare Analysis (qurAI) group. Join Us! The Quantitative Healthcare Analysis
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contribute to developing and/or applying artificial intelligence, machine learning, and image segmentation and/or data science-based methods in medical imaging processing. Salary Range: $47,000 - $70,500
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 11 days ago
resolution mammography images. The model should be trained using a combination of weakly supervised learning, for images without local annotations, and fully supervised learning, for images with corresponding