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The world is dynamic, in constant flux. However, machine learning typically learns static models from historical data. As the world changes, these models decline in performance, sometimes
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computers to large-scale multi-dimensional simulations on high-end supercomputers, depending on your interests and inclinations. "Modelling extreme supernova explosions: From fast and faint to bright and
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privacy-enhancing techniques such as secure multi-party computation, homomorphic encryption, differential privacy, and trusted execution to design algorithms and protocols to secure ML models within
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My research interests focus on the stars - primarily their structure, evolution and nucleosynthesis. This can involve modelling of mixing in stars, or effects of changing nuclear burning rates
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The brain is a complex machine and brain function remains yet to be fully understood. This project works at the intersection of dynamical modelling, statistical signal processing, statistical
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research and cybersecurity research, notably one current direction is on advancing the latest generative AI models for cybersecurity, or vice versa: using cybersecurity to attack AI. The student is free
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computational while other work will involve time spent in the lab. Search for physics beyond the Standard Model in penguin decays in data from the LHCb experiment. Identify particle identification requirements
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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. This project aims to enable the widespread adoption of such tools by: Aims Creating a pre-trained model that can detect rodent body part locations in videos of common behavioural tests, without requiring manual
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This is one of our CSIRO Next Generation AI graduate programme PhD projects with Future Wellness Group: https://www.monash.edu/it/ssc/raise/projects/personal-future-health-prediction Note: *** Must