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materials and we utilise these non-absorbed X-rays to massively increase image contrast and reduce radiation exposure using coherent synchrotron radiation. We have developed these “phase contrast” and “dark
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. The project will combine field measurements and laboratory analysis, using cutting-edge techniques including dynamic contact angle measurement, X-ray diffraction, mid-infrared spectroscopy, and synchrotron
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the pH range of 2.5 to 4.5. Advanced analytical training will be provided in techniques such as X-ray Diffraction (XRD), synchrotron-based spectroscopy and scanning electron microscopy (SEM). This PhD
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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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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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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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I supervise computational projects in electron microscopy imaging for investigating materials at atomic resolution. Some projects centre on analysing experimental data acquired by experimental
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" (with Prof Kris Helmerson) "Wide-field coherent phase imaging of AC magnetic fields" (with Prof Kris Helmerson) web page For further details or alternative project arrangements, please contact
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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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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