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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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hospital, through CMIV , with different types of expertise. The group also collaborates with other divisions at Linköping University; mainly the computer vision laboratory at the department of electrical
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image processing - Taste for interdisciplinary environment: physics, chemistry and biology - Good interpersonal skills (enthusiasm for research), able to work independently as well as in a team, taking
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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. Project Description Dynamic assessment of bone and joint motion remains a major challenge in musculoskeletal imaging. While MRI and CT provide high-resolution anatomical information, they are limited in
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due to a high Stoke’s shift, and a fast response time. In this project we will develop prototype perovskite scintillator gamma detectors fabricated from perovskite materials grown at Surrey. Perovskite
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 20 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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foundations of the understanding of animate and inanimate nature and the foundations of of natural science research that forms the paradigm of a particular historical epoch natural science. The history of each
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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to create knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position The Department of Civil and Environmental Engineering (IBM