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AI is now trending, and impacting diverse application domains beyond IT, from education (chatGPT) to natural sciences (protein analysis) to social media. This PhD research focuses on the fusing AI
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strong out-of-distribution generalization capability [2]. If user-specific information is identified and removable from the input data, the devised techniques can also be applied for privacy-sensitive
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catastrophically so. This PhD will develop technologies for addressing this serious problem, building upon our groundbreaking research into the problem. Required knowledge A solid grounding in machine learning
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Industry Innovation Program Scholarship The Embedded Co-Op Scholarship funded by an Industry Partner via the corresponding Faculty be introduced to allow industry and students to directly interact
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, software, human-computer interaction, ...). We also work very much interdisciplinarily with colleagues from other faculties, e.g. on bio-diversity matters, on physical aspects, on modelling aspects, and on
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". A number of emerging approaches, such as zero resource and unsupervised NMT, have investigated alternative methods in developing NMT models where sufficient parallel corpora are not available (eg [1,2
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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions
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for a fully funded, 3.5-year PhD scholarship for commencement in 2026. The scholarship provides a stipend of $52,352 per annum tax exempt for 3.5 years along with additional support. As part of
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Skip to main content Main Menu - Primary Home Projects Supervisors Expression of Interest Contact Computational drug discovery Primary supervisor Geoff Webb Research area Data Science and Artificial
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, pp270-283 Wallace, C.S. and D.L. Dowe (2000). MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions, Statistics and Computing, Vol. 10, No. 1, Jan. 2000, pp73-83.