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, this project answers the question: how can forest restoration-based environmental markets deliver net environmental and economic gains equitably? DESCRIPTION This PhD scholarship is part of an ARC DECRA project
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paradigms rely on a fragile "closed-world" assumption: that the unlabeled pool perfectly reflects the distribution of the labelled seed set. In real-world deployments, this is rarely true. Data streams
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hospital or population often fail when applied elsewhere due to distributional shifts. Since acquiring new labeled data is often costly or infeasible due to rare diseases, limited expert availability, and
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, and costs of running diverse applications in large-scale distributed systems. This project offers researchers and students a chance to explore cutting-edge concepts in AI-driven infrastructure
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missing modalities and distribution shift. Design uncertainty-aware decision frameworks for downstream tasks. Expected Contributions This PhD project is expected to contribute: Scalable Bayesian uncertainty
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, from swarm robotics to mesh networks. The prototypical model system for the investigation of self-organised task allocation are social insect colonies, such as bees and ants. They are able to distribute
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. We are seeking an experienced and motivated Lecturer in Science Education to join our dynamic School of Education. This is an exciting opportunity for an educator with a strong background in secondary
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Smart Stormwater Systems journey. You will help deliver a real-world demonstration site that turns award-recognised research into practice. The site will show how distributed storages, smart design and
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malformation, cerebral aneurysm, or tumour. ICH accounts for at least 10-15% of all strokes and is even higher pending ethnicity and global distribution. The overall incidence of ICH is reported to range between
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is highly complex. For the proposed PhD project, experimental data are already available that bring together maps of orientations of such crystals together with the deformation pattern generated during