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Description Join the Next Generation of Cancer Imaging Research We are seeking highly motivated students to join an exciting multidisciplinary program focused on the development of novel molecular contrast
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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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. The research combines robotics, computer vision, artificial intelligence, machine learning, control systems, and medical robotics to solve one of the most challenging problems in modern automation. Project
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culture, protein work, microscopy) and relevant area(s) A strong interest in oncology, tumour biology, pancreatic cancer, cell biology, molecular mechanisms, imaging A willingness to work with animal models
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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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-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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" "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
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extreme storms and sea-level rise. Project Background Coastal rock cliffs along Australia’s eastern seaboard are increasingly threatened by extreme storms, wave attack and rising sea levels. These processes
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approach that ensures both stealth and functionality. This project proposes to develop Stealth Integrated Sensing and Communication (Stealth-ISAC), a paradigm where communication and sensing signals remain