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Field
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
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an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject Area Medical imaging, biomedical engineering, computer science & IT
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features from multiple imaging modalities (CT, MRI, PET, ultrasound); (2) design advanced AI algorithms for early-stage cancer detection with high sensitivity and specificity; (3) create user-centric AI co
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will develop new mathematical tools to analyse brain imaging data using persistent homology, a method from topological data analysis (TDA) that captures the shape and connectivity of complex data across
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and challenging experiments inspire you? This PhD project is part of a research project that will develop and implement a focusing neutron optics prototype. Neutrons are inherently excellent probes
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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to image the centre of live, intact, plant roots. The ability to observe dynamic cellular processes at the centre of a live root for the first time will unlock entirely new lines of biological inquiry
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capabilities. Both conventional and ultra-high dose-rate proton therapies demand advanced imaging technologies to enable accurate in vivo three-dimensional dose verification and treatment delivery assessment. A
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: Experience with crystallization processes, soft matter physics, or porous media Familiarity with imaging techniques (optical/electron microscopy, X-ray tomography, or spectroscopy) Experience with cultural
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relevant field (Informatics, Computer Science, Ecology, or Biology). strong Python programming skills proven experience with Machine Learning and image processing (Deep Learning/Object Detection) strong