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application! We are seeking a highly motivated PhD student to join a research project at the forefront of battery diagnostics and modelling, that will help shape the future of battery technology by developing
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methods that can accurately model such processes remains an open and active research frontier. This PhD project is fundamentally about advancing that frontier, contributing new methods for generative
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. You will develop dynamic models and apply them, for example, to analyze sociotechnological networks and to model interactions between humans and AI agents (such as LLM-based chatbots and autonomous
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will build an experimental and computational platform based on 3D-printed, brain-mimetic tissue models with tunable transport properties, where interface transport can be measured and predicted
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
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emissions and biodiversity impacts related to bank lending, particularly in agriculture. Therefore, banks therefore often must rely on coarse standard values or low‑precision models, making it difficult
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student in this project, you will contribute to the development of new models and methods in machine learning for D-MIMO integrated sensing. This includes working with large amounts of data generated by a
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application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets
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-language-action models (VLA), specifically the handling of uncertainty in VLAs. VLAs have the potential to simplify system design in robotics and autonomous driving, both through verbal user interfaces, and
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scaling model sizes, training budgets, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on