273 linked-data-"https:"-"https:"-"https:"-"https:"-"https:" positions at Monash University
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follow-up honours project in this area. https://github.com/cormackikkert/CEGARBox https://github.com/cormackikkert/CEGARBoxCPP Rajeev Goré, Cormac Kikkert: CEGAR-Tableaux: Improved Modal Satisfiability
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Co-supervisors Shuo Wang Jason Xue Research area Cybersecurity Project description: Generative AI models work by training large networks over vast quantities of unstructured data, which may then be
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Co-supervisors Shuo Wang Jason Xue Research area Cybersecurity Project description: Generative AI models work by training large networks over vast quantities of unstructured data, which may then be
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Bionic neural engine. Compared to cloud-based machine learning services, on-device ML is privacy-friendly, of low latency, and can work offline. User data will remain at the mobile device for ML
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Bionic neural engine. Compared to cloud-based machine learning services, on-device ML is privacy-friendly, of low latency, and can work offline. User data will remain at the mobile device for ML
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similar time series - and to pool (or combine) relevant data. One of the approaches that will be used will the information-theoretic Bayesian minimum message length (MML) principle. Student cohort PhD
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similar time series - and to pool (or combine) relevant data. One of the approaches that will be used will the information-theoretic Bayesian minimum message length (MML) principle. Student cohort PhD
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type of document. Project funding Other Funding reference https://www.monash.edu/graduate-research/future-students/scholarships Learn more about minimum entry requirements .
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type of document. Project funding Other Funding reference https://www.monash.edu/graduate-research/future-students/scholarships Learn more about minimum entry requirements .
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body. #digitalhealth Required knowledge This project draws on a recent Dagstuhl Seminar (https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=18322) that brought together leading experts from