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- University of Oslo
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
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, computer vision or machine learning or Documented Experience with 2D or 3D biomedical imaging, quantitative or multimodal biological datasets. Familiarity with biomaterials, tissue engineering, scaffolds
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processing, computer vision or machine learning or Documented Experience with 2D or 3D biomedical imaging, quantitative or multimodal biological datasets. Familiarity with biomaterials, tissue engineering
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for the position. Preferred selection criteria Experience with machine learning and neural networks Basic knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux
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knowledge of MR physics Experience with signal processing and/or image processing Experience with Linux systems and High Performance Computing Good oral and written presentation skills in Norwegian
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application process here. About the position Are you motivated to develop next-generation research infrastructure at the intersection of robotics, mechatronics, sensor systems, and life science technology? We
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Signal processing and data analytics, control systems Machine learning or computer vision Product development in multidisciplinary engineering teams. Responsibility teams Exposure to globally leading
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. The project will involve the development, characterization and application of physiologically relevant models, including spheroids, organoids and tissue-engineered constructs, with opportunities to work with
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21 Sep 2026 Job Information Organisation/Company NTNU Norwegian University of Science and Technology Department Department of Civil and Environmental Engineering Research Field Engineering
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and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video unable to load from YouTube. Accept
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biomaterials, development of wear-testing methods, crown-material processing, mechanical characterisation, surface characterisation, micro-CT and quantitative image analysis. An interdisciplinary and