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Description Join the Next Generation of Cancer Imaging Research We are seeking highly motivated students to join an exciting multidisciplinary program focused on the development of novel molecular contrast
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PhD Scholarship in Multimodal Federated Learning and Medical Image Analysis Job No.: 695949 Location: Clayton campus Employment Type: Full-time Duration: 3-year and 3-month fixed-term appointment
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, conventional secure communication relies on encryption, which, while preventing data interception, still allows adversaries to detect and jam transmissions. This limitation underscores the need for an advanced
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processing and phase transformation. The candidate will work with experimental datasets from techniques such as electron microscopy, EBSD, X-ray diffraction, X-ray imaging and in situ characterisation
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offers an opportunity to contribute to prototype development, experimental aerosol characterisation, bench validation and translational evaluation of an emerging pulmonary drug-delivery platform. Research
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materials characterisation and modelling. Techniques may include thermogravimetric analysis, in situ X-ray diffraction, SEM, EBSD, image analysis, thermodynamic calculations and kinetic modelling. The aim is
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Roentgen’s Nobel Prize-winning discovery of X-rays enabled us to non-destructively image inside the body, birthing medical diagnostic imaging and revolutionising materials characterisation
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accuracy. By combining global visual information from an ophthalmic microscope with local images acquired by a miniature ophthalmic endoscope, the project will investigate how shadow-based visual cues can be
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Conventional x-ray imaging is firmly established as an invaluable tool in medicine, security, research and manufacturing. However, conventional methods extract only a fraction of the sample
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imaging, based on absorption, provides good image contrast between high- and low-density materials, such as bones and soft tissue. However, it cannot distinguish subtle density differences between soft