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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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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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for Molecular Imaging, Dept. of Biomedical Sciences, akjaer@sund.ku.dk . Co-supervisor is Post doc Karina Stræde, Cluster for Molecular Imaging, Dept. of Biomedical Sciences, karinastraede@sund.ku.dk . Start
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Biology, Biomedical Imaging, Biochemistry, Physics, or a related field A strong interest in biomedical imaging, contrast agent development, immune cell tracking, and data analysis Previous experience with
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This project aims to enhance best practices in strain quantification for biomedical applications, facilitating the transition of image-based measurement methods from laboratory research to clinical
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of biomechanics, biomedical imaging, and neuromuscular physiology in an interdisciplinary and international environment. Limited teaching within biomechanical engineering can be expected, but also in other study
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hub investigating the critical roles of ion channels—particularly the TRP superfamily—in physiological and pathological processes. Our interdisciplinary approach spans from foundational
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adults, significantly impacting quality of life, and remain challenging to treat. Our research group has pioneered innovative methods to study voiding behavior in mice and developed live imaging techniques
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project will be conducted in close collaboration with the Cardiac Physiology Group at the Department of Biomedical Sciences at the University of Copenhagen. This group is specialized in large animal models
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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