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approaches. Apply hybrid optimisation techniques (e.g., quantum-inspired or QAOA-based methods) to determine optimal intervention strategies under resource constraints. Compare the performance, scalability
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Anomaly detection methods address the need for automatic detection of unusual events with applications in cybersecurity. This project aims to address the efficacy of existing models when applied
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Statistical Methods, Automated Planning and/or Reinforcement Learning.
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Time series are an ever growing form of data, generated by numerous types of sensors and automated processes. However, machine learning and deep learning methods for analysing time series are much
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operators for these notions. Over the past fifty years, such non-classical logics have proved vital in computer science and logic-based artificial intelligence: after all, any intelligent agent must be able
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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roles in both Australia and Hong Kong. In a parallel sporting career, Chris was also a two-time Olympian in 1976 and 1980 and the Head Coach of the Australian Track and Field Team at the Sydney Olympic
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images