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This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods
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Contextual Data Analytics (ICDA) as a method to address contextual analysis challenges by bringing rich contextual information to the analyst’s workspace. Despite the technological capability to support ICDA
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while inferring underlying physiological changes. Required knowledge Machine learning, dynamical systems theory, control theory, signal processing, time series analysis, neuroscience are all relevant and
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component involving medical practicioners and providers, and also network / telecoms providers, to collect the required datasets of applications, services, and geographically available QoS data. The technical
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The United Nations Development Programme has identified access to information as an essential element to support poverty eradication. People living in poverty are often unable to access information
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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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inform or design future experiments. As a researcher in my group, you would not only develop imaging theory and analysis tools to answer science questions about where the atoms are, what they are, and how
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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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feedback using storytelling elements?