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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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will work on scene understanding using RGB and possibly thermal and radar images, including based on object detection and image segmentation, and collaborate effectively with other technical partners who
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. Biomolecular condensates have emerged as a new paradigm to understand biological functions in living cells. Dresden has pioneered research in the field of biomolecular condensates and developed into its vibrant
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knowledge or hands-on experience with Python, ImageJ, and/or MATLAB is highly beneficial for image processing and data analysis. Communication: Excellent written and spoken communication skills in English are
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lengthy processing times associated with sequencing. This PhD project aims to develop innovative artificial intelligence (AI) methodologies by integrating histopathology images and RNA sequencing data
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the Norms for Grant Assignment and Management within R&D Projects, by the Code of Administrative Procedure (CPA), all in their current wording, and by other applicable national and community legislation
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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imaging technologies, such as cryo-electron tomography (cryo-ET) and expansion microscopy, have been pivotal in advancing our understanding of the structure and function of motile cilia. In contrast
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like tufting, machine-based embroidery techniques or non-interlaced 3D pre-forming. Development of advanced imaging and characterization technologies (X-ray micro tomography, EPR imaging and spectroscopy
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synthesizing detectable targeting ligands for engineered immune cells and image and quantify the target cell engagement and efficiency. The project is highly multidisciplinary and involves aspects of synthetic