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of Sciences is looking for a full-time (100%) doctoral scholarship holder in the field of medical image processing and analysis using deep learning. This position is part of the ERC Consolidator project “A data
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Research Associate in Image and Vision Processing School: School of Computing, Engineering and Intelligent Systems Campus: Derry/Londonderry Salary: £31,414 - £39,369 per annum Duration: Fixed-term
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their supervisor before submitting an application. Who are we looking for? We are seeking a Process Analyst to join Digital Business Solution, Administrative Division. The analysis and redesign of enterprise-wide
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engineering research. Project Overview Our vision is to drive the future of nuclear medicine by advancing the development and preclinical characterisation of tracers, redefining image acquisition, and
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engineering research. Project Overview Our vision is to drive the future of nuclear medicine by advancing the development and preclinical characterisation of tracers, redefining image acquisition, and
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the world’s top universities and is regularly named one of Canada’s Top 100 Employers and one of Canada’s Best Diversity Employers. Your opportunity: The Process Improvement Analyst will work under the
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consortium that develops methods of applied mathematics and physics for the benefit of society, including medical imaging, industrial process monitoring, non-destructive structural testing and satellite data
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University of Tennessee Medical Center at Knoxville | Knoxville, Tennessee | United States | about 2 hours ago
Job Detail Right-click for referral link Job Title: Sterile Processing Tech I Department: Sterile Processing Full/Part Time: Full Req Number: UHS-31060 Position Summary: Provides clean and sterile
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including: tissue collection, trimming, processing, and embedding, paraffin/frozen sectioning, staining, and immunohistochemistry/immunofluorescence. Additionally, the technician will use microscopy/imaging
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Modern deep learning techniques achieve human-like performance in many medical image analysis tasks, including the identification of anomalous tissue/pathology from medical scans. To be trained