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segregation, interface-related energy losses, and insufficient process reproducibility. Addressing these challenges requires a workflow that connects rapid materials and process exploration with rigorous device
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very challenging, active matter research presents great opportunities for finding novel physical mechanisms and for using such systems in possible applications. In this PhD project, we will combine
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physically realistic datasets for AI training, benchmarking, and validation. The successful candidate will contribute to AI.Grids, a flagship European initiative on the application of Artificial Intelligence
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evaluation strategies. In close collaboration with chemists, engineers and data scientists, a platform is being developed that combines materials development, process optimisation and machine learning
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a fully operational computing stack with a flexible, renewable-based energy system incorporating both battery and hydrogen storage. Alongside the physical prototype, a comprehensive co-simulation