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This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized
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the development of Explainable AI Systems that can provide explanations of AI agent decisions to human users. Past work on plan explanations primarily focused on explaining the correctness and validity of plans. In
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discovery. While deep generative models have shown promise in proposing novel molecular structures, they typically require massive, cleanly labelled datasets to train effectively. In practice, acquiring high
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This research focuses on developing and evaluating methodologies for the optimal design of control charts within the framework of Statistical Process Control (SPC). The study aims to determine the
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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the construction of PRS and enhance disease prediction. Students will gain experience in: Statistical genetics and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and
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these challenges, calling for the development of responsible AI systems that are transparent, trustworthy, and aligned with human values in educational contexts. This PhD project aims to design, develop, and
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these issues is critical for building trustworthy multimodal AI systems. Research Objectives The goal of this PhD project is to develop scalable Bayesian uncertainty estimation frameworks for single- and multi
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algorithms for computing MML solutions beyond the one-dimensional case. Extend existing dynamic programming approaches to higher-dimensional problems or develop novel approximation methods that preserve
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formula is true or false (EXPTIME vs NP). Can we develop and implement efficient algorithms for this problem? This problem has been attacked using multiple different methods for the past 40 years, without