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-intensive applications. Researchers in this field design robust, scalable architectures and pipelines to transform raw data into reliable, accessible formats, leveraging techniques like real-time streaming
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& harmonisation — Using text, images, omics, citizen science Explainability & credibility — Interpretable, trustworthy models; links to causal inference Robustness in non-i.i.d. data — Reliable learning under
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22.12.2025, Academic staff We are an interdisciplinary team at the Chair of Safety, Performance and Reliability for Learning Systems, and we are looking for exceptional PhD candidates to join our
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. The performance of the developed methods will be evaluated using real operating data. In addition, it will be investigated how reliability and safety conditions can be taken into account. This is particularly
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environment perception and your own state estimation, and then integrating it into a newly developed trajectory and behavior planner. The goal is to enable safe, reliable and highly dynamic vehicle actions in
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, reliable and committed team -Numerous opportunities for professional and interdisciplinary training (e.g. soft-skill seminars) -Modern working environment and infrastructure -Possibility to work abroad