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of masculinity, especially in relation to younger male audiences. The successful applicant may explore such questions as: What drives engagement with manosphere communities? How do algorithmic systems and platform
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formative assessment and personalised feedback while ensuring fairness, accountability, and transparency. The research will explore a combination of algorithmic design, human–AI interaction, and empirical
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estimation methods for deep neural networks. A principled Bayesian framework for multimodal uncertainty modeling. Robust learning algorithms under missing modalities and distribution shifts. New uncertainty
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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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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
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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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the given non-classical logic. The proof of the claim contains an algorithm for deciding whether an arbitrary formula is true or else false! This proof can then be exported automatically to produce a formally
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group of experts to predict (probabilistically) whether these occupations will be automated, augmented or unaffected by emerging technologies. Using this data, a classification algorithm is then trained
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publicly available datasets; 3) Proposing algorithms aimed at improving the accuracy of human activity detection; 4) Implementing these algorithms, evaluating their performance empirically, and comparing
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and