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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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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
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domain experts’ beliefs about the relationships among variables that can be used to describe them. The BN structure, the probability distributions and parameters it is built from, can be derived from data