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vehicles and achieving net-zero targets, but they degrade over time, reducing performance and safety. A key issue is gas generation, which causes internal pressure build-up and can lead to cell failure, but
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in complex biological networks e.g. food webs, forming the foundation of natural ecosystems. Yet, we lack the tools to predict how these networks change in time and space. This is especially critical
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on developing and studying privacy-preserving methods, such as differential privacy, Bayesian privacy, federated learning and synthetic data. The aim is to enable meaningful analyses, such as identifying disease
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for Bayesian/statistical modelling with uncertainty analysis or programming skills in Python, MATLAB or equivalent and a background in hydraulic, kinetic or systems models. Also highly desirable to have
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systems, neuroscience, and safety and security. The Division of Systems and Control enjoys a wide network of strong international collaborators all around the world, for example at the University of Oxford
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research of good quality within the framework described above Academic publications and popular science dissemination Participate in the aiD research network and engage with Statsbygg Contribute to research