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their applications to problems in natural sciences (physics, chemistry) and practical computing (machine learning, AI, optimization), and methods to make them compatible with near-term quantum devices
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algorithms; experience in 3D/4D (X-ray tomography) image processing; experience in machine-/deep-learning based image analysis; knowledge of tomographic reconstruction methods; experience in materials research
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radiation oncology faculty member, perform a variety of data entry and related analyses. Responsibilities: Acquire fluorescent microscopy images Perform image analysis Develop a lab supply list and inventory
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and Computer Sciences at Heriot-Watt University is recruiting a postdoctoral researcher in statistical machine learning for computational imaging. The position is open in the context of the 5-year
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ensure proper operation and maintenance of equipment. Job Requirements: Have relevant competence in the areas of Deep Learning/Computer Vision Have a Bachelor/master’s degree in computer science
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of both Apple and Windows platforms. Travel to off campus locations is required. Job Duties: Manage the computer technicians. Design and develop computer deployment packages and images using deployment
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://ucnet.universityofcalifornia.edu/compensation-and-benefits/index.html Department Description The UCSF Department of Radiology Imaging IT team architects, implements, and manages the computer systems and networks needed to support
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 4 hours ago
, Applied Mathematics, Physics, Biophysics or a similar qualification Interest in and/or experience with AI/machine learning A willingness to learn and extend this knowledge and master new challenges in
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Archival Computer System (PACS), Digital Mammography. Working knowledge of Radiology Information system (RIS), and Hospital Information System (HIS). Complete certification training program for Radiologic
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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative