647 information-security-"https:"-"https:"-"https:"-"https:"-"https:"-"Bournemouth-University" positions at Monash University in Australia
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background in AI/ML, data science, or signal processing Interest in music informatics, emotion modelling, or multimodal AI Ability to implement and evaluate machine learning models independently Commitment
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AI is now trending, and impacting diverse application domains beyond IT, from education (chatGPT) to natural sciences (protein analysis) to social media. This PhD research focuses on the fusing AI
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conditions at: www.monash.edu/graduate-research/study/scholarships/fees-scholarships/scholarship-policy-and-procedures . The Opportunity This PhD scholarship is funded as part of the Monash Initiative
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Antimicrobial resistance (AMR) is one of the most significant and immediate threats to health in Australia and globally. As an Infectious Diseases physician and researcher, the second supervisor is
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operational needs. There is a key opportunity for specialist work in an emergent intersection area which we can call Value-Based Digital Health (VBDH). To expand further, VBDH is a discipline area that sits
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coordination, and research planning and will work closely with researchers, clinicians and students. The position is responsible for coordinating a broad range of research and administrative functions, including
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, cardiovascular disease, diabetes, neurological disorders, metabolic diseases, and reproductive health. This is a great opportunity to contribute your knowledge and expertise to the world of research at Monash
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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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Authorised by: Marketing, Faculty of IT , Monash University . Maintained by: Marketing, Faculty of IT . Copyright © 2024 Monash University. ABN 12 377 614 012 Accessibility - Disclaimer and
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Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is