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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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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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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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image analysis, including AI-enabled approaches. The successful candidate will participate in human-subjects-based translational research, including analysis of ophthalmic imaging (e.g., OCT), clinical
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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
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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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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
public research institutions in the country. UNC-Chapel Hill offers postdocs comprehensive medical and vision coverage , paid leave, and benefits and services that support professional development and a
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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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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