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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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algorithms to achieve real-time control and process optimisation of drainage pipe networks. It seeks to improve the operational efficiency of urban wastewater systems, reducing energy consumption and extending
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processes that arise in light–matter interactions, and to use these frameworks to extract fine spectral or temporal information from weak or structured optical fields. The successful candidate will work
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will undergo an interview process. If supported to apply, you will then submit an expression of interest (EOI) following the advice at how to apply for a research degree . In your EOI, nominate Dr
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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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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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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
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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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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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phenomenon that has the potential to shift an established photochemical paradigm. The research will involve a range of computer modelling methods and techniques in the areas of theoretical and computational