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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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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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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
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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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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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to life in breakthrough experiments—please reach out. "Quantum nanophotonic chips" "Structured-light imaging and spectroscopy” “Meta-optics and meta-waveguides" "2D materials and Lightwave valleytronics