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Field
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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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Species’ distributions are shifting in response to global climate change and other human pressures. Accurate methods to monitor and predict distribution shifts are urgently needed to manage
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management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research. Key Components and Example Scenarios Predictive Resource Allocation and Load
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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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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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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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skills in Git, CI/CD pipelines, and infrastructure automation tools such as ArgoCD, Ansible, Azure DevOps, and Github. Solid foundation in Linux systems, distributed systems and high-performance computing
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-on experience with large-scale data processing using distributed computing frameworks. Strong understanding of data performance optimisation techniques. Proficiency in Python and SQL, with experience using Git
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material, such as event proposals, run sheets, briefing notes and guest lists and ensure these are distributed in a timely manner. Collaborate with Advancement colleagues on pre‑ and post‑event
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datasets to generate insights into infrastructure demand, capacity, performance, and spatial distribution. This includes developing and maintaining data pipelines for high-volume infrastructure and spatial