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Computational Imaging for High-Throughput Applications (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of the Leiden Institute
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of coronary artery disease and supporting clinical decision-making during catheterization procedures. The project brings together cardiovascular imaging, generative AI, and computational modeling to develop
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using optical tweezers and confocal fluorescence imaging, building on previous work (O’Brien et al., Nat Comm 2024). Together with our collaborators, you will produce materials for these experiments and
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advancing medical imaging and improving cardiovascular care? Join us in developing a revolutionary multi-aperture ultrasound platform that enables comprehensive 3D imaging and biomechanical assessment
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) technology offer new opportunities to improve the diagnostic quality of chest imaging. With its ability to provide ultra-high-resolution (UHR) images and spectral information, PCCT has the potential to enhance
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environmental conditions, and develop and validate computational decoders that recover environmental history from an image of the living organism. Based on the found capabilities, the candidate will also design
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-inspired computing paradigm with the potential to drastically reduce energy consumption while enabling faster inference than conventional digital architectures. A major challenge, however, is the development
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– AI-generated text, audio, images, and video. However, given their still developing cognitive skills, they may also be least equipped to deal with synthetic content. This project investigates how
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machine learning and physics to recover nanoscale information from imperfect images? Modern computer chips are built with features only a few nanometers across, yet manufacturers need to measure these
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Our team We develop next generation optical imaging and sensing platforms in the context of future biomedical, pharmaceutical and clinical applications. We are particularly interested in label-free