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rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is characterized by close
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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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-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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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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is characterized by a modern view of the statistical subject, where probabilistic models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view
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teaching in key and rapidly evolving areas such as autonomous systems, data-driven modeling, learning-based control, optimization, complex networks, and sensor fusion. Research at the division is
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a modern view of the statistical subject, where probabilistic models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view of machine learning
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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area of AI security, on the topic of “Memory Poisoning in LLM Agents: Foundations, Attacks, and Defenses”. Your work assignments Large language model (LLM) agents represent the next generation of
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. The employment requires strong subject knowledge in optimization, mathematical modeling, and quantitative analysis. You are a problem solver who works well with complex issues, understands complicated written