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planning algorithms for GPS-denied lunar environments and extreme operational conditions stochastic optimisation frameworks for mission-critical decision-making under uncertainty Research areas and technical
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responsibilities will be to: conduct high-quality research in intelligent sensing and control for complex project environments develop and implement AI-based algorithms (e.g., GNNs, deep reinforcement learning
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of the algorithms developed in this project. About you The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance
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understanding of non-stationary complex systems through theoretical analysis and numerical simulation develop efficient statistical algorithms for analyzing and inferring dynamical models from multivariate time
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algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating interpretable insights through novel analytics and
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, Medicine, or Engineering) demonstrated experience or knowledge of one or more of the following: computational algorithm development working with medical images, in particular CT or cone-beam CT a
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and coatings. You will be given the opportunity to work collaboratively with a motivated team of early career researchers and PhDs on optical low-adhesion coatings. Within the team, you will be given
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on the skills and experience of the candidate. We are interested in applicants with a PhD, with a strong research track record relative to opportunity, and with disciplinary expertise in public health
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education-focused space. Selection Criteria: a PhD in an arts, social sciences, or a higher-education specialisation experience in partnership-based research and teaching engagements with industry
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, Medicine, or Engineering) demonstrated experience or knowledge of one or more of the following: computational algorithm development working with medical images, in particular CT or cone-beam CT a