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for this position. Skills: Computed Tomography (CT) Image Processing, Magnetic Resonance Imaging (MRI) Driver's License: A driver's license is not required for this position. More About This Job WashU seeks highly
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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
, please visit: https://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html Required Qualifications Graduate of a formal radiology technology training program from an AMA approved school
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the MediTwin project, which aims at advancing patient-specific digital twins for medical applications by combining physics-based modeling, data assimilation,and efficient computational pipelines (https://www
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searches using various computer systems. Assists in editing scientific publications, abstracts and/or posters. Audits and screens vendors/suppliers to evaluate the quality of products and equipment
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data sets (genomics, proteomics, imaging, neuroscience), and related areas at the interface of computer/data science and the life sciences. Applicants must hold a Ph.D. in Engineering, Computer Science
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California State University, Long Beach | Long Beach, California | United States | about 13 hours ago
achievement; building community; and cultivating resilience. College of Engineering Department of Computer Engineering and Computer Science (CECS) Required Qualifications: Ph.D. in Computer Science, Computer
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, perform data analysis utilizing medical imaging data, computer models, and statistical tools. The individual will prepare manuscripts and contribute to the development of grant proposals. The McIntyreLab
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partner, POST Luxembourg , one of the country's largest employers and a leading provider of postal, telecom, ICT, and financial services. The successful candidate will join the Computer Vision, Imaging, and
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computer vision methods and their applications Your Profile: Excellent Master’s degree in engineering, computer science or mathematics (or a related field), with a focus on computer vision, image processing
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, implementation, and analysis of machine learning models for computer vision tasks (40%). Analysis of natural scene statistics in aquatic and terrestrial environments (40%). Design of models to learn texture