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anomalies in evolving graphs. In this research proposal, our aim is to explore the parallels of deep learning and anomaly detection in dynamic graphs. In particular we are interested to redesign deep neural
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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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content creation software, assessment designs, evaluation methods, multimedia learning theory, universal design principles, and project management. For candidates that demonstrate a quality track record of
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Study lipid–protein–polysaccharide interfaces in complex biological media Conduct research involving cell culture, bacteriology, and compositional analysis Develop and optimise equipment and methods
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Experience in management of a team of finance professionals in delivering high quality level of service Excellent analytical, numerical, research and problem-solving skills to achieve desired outcomes
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climate economy in Australia (Dr Svenja Keele ) Communicating Data to Advance Community and Ecological Wellbeing (Prof. Libby Lester ) Making Futures Together: Designing and Testing Creative Methods
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in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1
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Project Description Recent advances in mixed reality (MR) technology, which seamlessly blend the physical environment with computer-generated content around the user, have reduced the barriers
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software frameworks, algorithms, robust testing and validation methods, and/or empirically validated solutions that contribute directly to social good, promoting trust, fairness, transparency, and
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tissues or reveal micro- or nano-structural features, like the small air sacs in lungs. To overcome these limitations, alternative X-ray imaging methods have been developed: X-ray phase-contrast and dark