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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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, and regenerative constructs. The project combines advanced 2D and 3D bioimaging, including micro/nanoCT, confocal microscopy and SEM, with computational image analysis, computer vision and machine
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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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application process here. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position Are you motivated to develop next-generation
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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. Proficiency in standard molecular and cell biology techniques, including RT-qPCR, ELISA, immunocytochemistry, and Western blotting. Experience with sample preparation, imaging, and image analysis using confocal
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scene, and leveraging that understanding for localization and navigation. A robot builds a picture of its surroundings one lidar scan at a time. Each scan on its own says very little; together
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and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured biological data are increasingly common in modern