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Field
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(e.g., tensor or multi-modality data). Work with other researchers and PhDs to develop AI applications related to image processing, computational imaging as well as computer vision. Lead a team of junior
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(e.g., tensor or multi-modality data). Work with other researchers and PhDs to develop AI applications related to image processing, computational imaging as well as computer vision. Lead a team of junior
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computing. With extensive experience in medical image analysis, computer vision, and AI systems through collaborations with leading institutions. Key Responsibilities: Conduct advanced research in the areas
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, operation of the scanner, data acquisition and analysis, image evaluation, and statistical analysis. The fellow will also be expected to prepare manuscripts and conference abstracts related to projects and
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workflows. (Preferred) Basic skills in microscopy image analysis and quantitative data processing. To Apply Applicants should submit: A CV including publications A cover letter describing relevant experience
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-disciplinary team of researchers, including bioinformaticians, pathologists, oncologists, and computer scientists, and conduct original research on computational pathology. Digital pathology images contain rich
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in image processing, quantitative analysis, and biological interpretation Proficiency in AI/machine learning tools for image segmentation, transformation, registration, or tracking Solid mathematical
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. The regulation will be applied in the processing of the applications. About the PhD position: About the PhD Research Fellow The fellowship will be for a period of 3 years, with the possibility for a 4th year
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requirements for the position The University of Bergen is subject to the regulations for the export control system. The regulation will be applied in the processing of the applications. About the PhD position
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collaboration with Industry to develop a prototype gas adsorption rig for CO2 capture applications. About the role The post holder will develop a prototype adsorption-based system for CO2 capture. The candidate