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Optimisation methods, such as mixed integer linear programming, have been very successful at decision-making for more than 50 years. Optimisation algorithms support basically every industry behind
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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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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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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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algorithms and techniques and design, implement, test, and maintain software/tools embodying those methods. You will prepare and submit grant proposals to external funding bodies. This position will also
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learning, algorithms, and programming. Prior exposure to reinforcement learning or human-robot interaction is highly desirable, though motivated candidates with a strong grounding in AI/ML and willingness
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and innovation catalyst, in this exciting project, you will develop novel algorithms to monitor and analyse workers' movements, detect harmful movement patterns, and implement simple intervention
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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 headspace website. Possible approaches to addressing this challenge might include: Developing algorithms to identify patterns and preferences based on service users’ previous content engagement
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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