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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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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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testing and assessment of device-assisted aerosol delivery under clinically relevant operating conditions. Data processing, image analysis, uncertainty assessment and experimental interpretation using
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-field imaging of dynamic processes" "Multi-scale X-ray speckle-based imaging" "Spectral X-ray speckle-based imaging" "Single-shot multi-projection X-ray phase-contrast imaging" "X-ray virtual histology
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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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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: [email protected].
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PhD Scholarship Opportunity - Processing intelligence for green metals using in situ X-ray characterisation and machine learning Job No.: 693787 Location: Clayton campus Employment Type: Full-time