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learning, materials characterisation or computational materials science. Previous experience with machine learning, computer vision, graph neural networks, Python, SEM/EBSD, XRD, image analysis
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Image processing and computer vision Experimental data analysis and uncertainty quantification Piezoelectric actuation, acoustic systems or electronic driver development Eligibility and Project
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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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field” imaging techniques to solve many important problems in biology and change clinical practice in respiratory medicine. Our ongoing research program involves developing new imaging technologies
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analytical imaging methods, then working with collaborators to apply these methods to biomedical research, diagnostic imaging and beyond. Research projects vary from purely theoretical, to computational
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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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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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, computer vision, federated learning, foundation models, adaptation techniques, multimodal learning, longitudinal image analysis or related areas, evidenced through coursework, research projects
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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