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letter of offer for the program if all conditions have been satisfied. About the scholarship Project details The project investigates distributed and edge intelligence for smart grids with distributed
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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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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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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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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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distribution functions. Total content of H2 in the solar neighbourhood and nearby galaxies and how they compare to other gas/dust tracers. Extinction mapping from resolved stars in nearby galaxies. Studying mass
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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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enabled through science-based, collaborative and practice-oriented research. The program builds on nearly a decade of Living Lab interventions across Indonesia and Fiji through the Revitalising Informal
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sources. Hands-on experience with large-scale data processing using distributed computing frameworks. Strong understanding of data performance optimisation techniques. Proficiency in Python and SQL, with