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the complexity of relevant structured classes of distributed game equilibrium problems. Develop data-driven decomposition methods for structured game equilibrium problems. Develop data-driven, fast and scalable
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fundamental challenges in reliable, resilient, and sustainable connectivity. You will join an international research environment that values scientific excellence, creativity, meaningful collaborations, and
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are poorly understood and not accounted for in current-generation hydrological models. The PhD research project will address these issues for more reliable hydrological predictions with a focus on the
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with incomplete information. Synthetic data offers a way out: realistic-but-artificial datasets that preserve the statistical, temporal and physical structure of real energy data without identifying real
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consortium led by Allseas that is developing a micro High-Temperature Reactor for maritime and land-based applications. The reactor is a pebble-bed HTR designed to provide reliable and safe energy production
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into fewer, far more reliable logical ones. Without effective QEC, adding qubits does not add computing power, because errors accumulate faster than machines can scale. Despite impressive hardware progress
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in grasslands (NutNet) and along landscape gradients in forests (Veluwe), as well as in response to landscape-scale manipulation of forest structural heterogeneity along a nitrogen deposition gradient
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evidence generation and reassessment. You will focus on how international HTA bodies anticipate it will shape their activities in the coming years. Assessing the extent to which GenAI tools can reliably
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, current tear-collection methods can alter tear composition and prevent reliable measurements, limiting the clinical adoption of tear-based diagnostics. In this PhD project, you will develop innovative
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groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic