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Identifying vulnerabilities in real-world applications is challenging. Currently, static analysis tools are concerned with false positives; runtime detection tools are free of false positives but
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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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increasingly taking place here as well. Early identification of infected colonies is a crucial component for treating infections effectively. Unfortunately, this currently requires very labour intensive and
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question and answer component to an existing MAPF visualiser as part of creating XMAPF. Required knowledge - Comfortable with discrete mathematics and proofs - Basic knowledge of AI (e.g., FIT3080
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challenge is to create disentangled representations for paralinguistic information and the content of speech. Herein, a component of such a disentangled representation contains only necessary information
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feedback using storytelling elements?
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Nanotechnology (IF=39.21), Brief in Bioinformatics (IF=11.62), Hypertension (IF=10.19), IEEE Transactions on Pattern Analysis and Machine Intelligence (IF=24.31), IEEE Transactions on Medical Imaging (TMI
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reconstruction and data analysis. The PhD students will be working at Monash Biomedical Imaging and Faculty of Information Technology, Monash University. Monash Biomedical Imaging is one of the most advanced
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points of study) for the Master of Engineering component. Number offered 10 scholarships available per year. Selection criteria Award to the highest-achieving Year 12 students, based on academic
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are expected to be both (a) evidence of validated (or otherwise) replicated studies, and (more importantly) (b) an analysis of factors underlying the multi-dimensional space of experimental methodologies