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the flexibility of neural methods. If successful, the work has the potential to advance applications such as automated theorem proving, knowledge-graph inference, and causal analysis. The Department of Computing
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models also provide uncertainty estimates in the model and its predictions, allowing the confidence in automated decisions to be evaluated. The goal of this project is therefore to develop learning
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methods that reduce compute, energy usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and
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approaches, such as geophysical methods, to capture internal hydrological processes. The project is conducted in close collaboration with municipalities and international research partners, and results will be