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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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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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work assignments A wide variety of physical phenomena like radio transmission, ultrasound, acoustics, or tsunami modelling involve the solution of partial differential equations (PDEs) that model wave
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, data-driven control in high dimensions has penetrated many new application areas. Examples include control of autonomous vehicles based on video data, simulation-based prediction of turbulent flows 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
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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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. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning - constitute fundamental scientific domains that act as
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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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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