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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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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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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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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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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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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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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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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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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