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The Department of Medical Imaging is one of the most active research departments of the Radboudumc. More than 100 researchers are continuously striving to optimize healthcare. Close cooperation
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is to investigate localized flow and crystallization processes of PCMs in devices under different conditions with advanced 3D imaging tools like CT and NMR imaging, and to couple this with performance
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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characterizing defects such as dislocations Applying generative models (e.g., GANs, diffusion models) to augment microscopy datasets Investigating domain adaptation techniques across different imaging modalities
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adipose tissue. In particular, we will study the role of different membrane receptors and their signaling pathways in the browning process. The various techniques used will include cell biology and genetic
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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, diffusion models) to augment microscopy datasets Investigating domain adaptation techniques across different imaging modalities Collaborating closely with experimental partners to validate methods and
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efficient for medicine? If the answer is yes, please continue reading! Join our team! We are looking for a PhD student to work on the topic of shape analysis for medical imaging, tailored for deep learning
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candidate with experience in cell culture, stem cells, molecular biology, immunohistochemistry, biochemistry, protein purification and/or confocal imaging. Experience in using human tissue is advantageous. A