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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized
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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find
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. This would provide thousands of diverse example images with corresponding body part locations. These data would be used to train a deep learning model 5, 7 . The model’s high-quality body part predictions may
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methods, and, more broadly, in the physics of imaging and materials science. The position will work in close collaboration with Professor Jian-Min Zuo, Director of the Monash Centre for Electron Microscopy
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, and, more broadly, in the physics of imaging and materials science. The position will utilise instruments in the Monash Centre for Electron Microscopy which has a powerful suite of instrumentation
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deliver high-quality teaching across undergraduate and postgraduate programs—including honours and Higher Degree by Research (HDR) supervision—with a strong focus on anatomy, imaging anatomy, and
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Allied Health Care is on the lookout for their next Lecturer/Senior Lecturer to join their team within the department of Medical Imaging and Radiation Sciences. This 6.5 month opportunity aims to prepare
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This project aims to develop robust algorithms capable of identifying and analyzing fingertips extracted from both static images and video footage. Machine learning techniques, particularly computer
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internationally recognised TRACK-FA dataset, applying computational modelling and neuroimaging analysis to explore brain structure-function relationships and identify imaging biomarkers that could inform future