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Field
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-group working on microbubble physics and will contribute to the guidance and supervision of PhD students involved in microbubble production, functionalization, physical modeling, and ultrasound imaging
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to self-supervised learning, medical imaging, and population-level phenotyping. About You You will have or be close to the completion of a relevant PhD/DPhil (e.g. in Computer Science, Engineering, or
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combining CRISPR-based endogenous protein tagging, advanced quantitative imaging, and biochemical approaches, this project seeks to uncover novel principles of replisome plasticity. A key long-term objective
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application date. Documented pedagogical experience. Experience in image analysis and/or computer vision, especially in the context of medical imaging Development, implementation and validation of AI tools and
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
radiologists involved in the projects. Training and research environment The Brain Imaging & Neuro Epidemiology group (BraINE) develops advanced methods for medical images analysis, combining computer vision
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resolution imaging facility (incl. two-photon microscopy, 3 to 9.4 Tesla fMRI) for human and animal research purposes, as well as an excellent, multidisciplinary scientific environment. Your profile: PhD in
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medical images and clinical text. The primary focus of this role is to develop foundation models that can be applied downstream to clinical triaging tasks—helping prioritise cases based on MRI imaging data
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and setup: Within the project, we follow a multidisciplinary collaborative approach for which we are have recruited 3 PhD students focusing on material science, advanced in vivo imaging and computation
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(but are not limited to) Computer Science, statistics, mathematics, automation, informatics, and Engineering. Experience in deep learning, machine learning and medical imaging processing Programming
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interdisciplinary research project in molecular ultrasound imaging using functionalized, monodisperse microbubbles. The project aims to understand how the acoustic response of microbubbles changes upon molecular