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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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package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and
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Neuro-symbolic AI combines the strengths of neural and symbolic methods to efficiently learn and reason over models of the world. Typically, many of the assurances that can be provided by
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Neuro-symbolic AI combines the strengths of neural and symbolic methods to efficiently learn and reason over models of the world. Typically, many of the assurances that can be provided by
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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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This project investigates 3Vs of Big Data (e.g Volume, Variety, and Velocity). Volume: Due to the exponential increase in data volume, it is necessary to adopt parallelism techniques to achieve
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DNA or RNA motif discovery is a popular biological method to identify over-represented DNA or RNA sequences in next generation sequencing experiments. These motifs represent the binding site
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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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Dowe, 1999a) ensures that - at least in principle, given enough search time - MML can infer any underlying computable model in a data-set. A consequence of this is that we can (e.g.) put latent factor
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Zealand Intensive Care Research Centre (ANZIC-RC) – a world-leading clinical research methods centre based at the Alfred Hospital campus. The Opportunity We are seeking a motivated and skilled Senior