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engineering background who has an interest in image reconstruction and/or modelling the mechanics of the heart. The project also has a sub aim of protecting the heart from radiation during radiation therapy
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of geometric flows to resolve important geometric and topological questions, and produce new theoretical tools applicable to similar systems arising in areas such as image processing, finance and material
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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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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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and metabolic reprogramming—using advanced computational and imaging tools. Ultimately, the candidate will contribute their findings to the ADAPTS pipeline, supporting the development of a real-time, AI
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, statistical and bioinformatic algorithms for the analysis of microscopic images and data obtained by various biochemical and biological assays. You will be part of and mentored by a multidisciplinary team
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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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markers/sensors and ii) automated assays based on high-content imaging systems to screen diverse chemical libraries for new and innovative bioactive molecules. This project will be highly collaborative and
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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 Innovation and of
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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