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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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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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Current reseach is in the areas of: Development of biomimetic structures as ultrasound contrast agents Deep tissue imaging using photoacoustic contrast agents All optical photoacoustic sensors
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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imaging, flow cytometry etc. The chance to forge collaborations with experts in diverse fields at the Adelaide biomedical precinct. Ample professional, scientific and mentoring opportunities to develop
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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
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contribute to research and policy addressing the escalating issue of the consumption of image-based sexual abuse (IBSA) and child sexual exploitation material (CSEM) online. The project is based at Flinders
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Qualifications Master or Honours About Swinburne University of Technology Swinburne’s strategy draws upon our understanding of future challenges. We choose to build Swinburne as the prototype of a new and
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are molecules that exist in two forms, referred to as enantiomers, each being a mirrored image of the other. Different enantiomers share the same physical properties, but often have starkly different biological
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of Technology Swinburne’s strategy draws upon our understanding of future challenges. We choose to build Swinburne as the prototype of a new and different university – one that is truly of Technology, of