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are realized and identify opportunities for further refinement. Develop a robust quality management framework for your AI-solutions, ensuring they can be safely evaluated, validated, and integrated within our
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controlling their noise is critical. This PhD focuses on airborne noise source localization in urban environments, enabling quiet air mobility. Job description The rapid growth of air mobility operations
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? Join us to develop deep learning techniques for fusing acoustic sensor data with other vehicle sensors for robust multi-modal environment perception. Help shape the future of autonomous driving! Job
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simulation frameworks combining electro-thermal-mechanical models for fast charging and robust performance. A key goal is a hierarchical design environment (cell → module → pack → vehicle) enabling flexible
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parametrisation. The models are compared to benchmark models and analysed regarding robustness and sensitivity. In a second step, the models will be tested under different climate change scenarios. The newly
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, enabling energy-efficient, quiet, and long-duration monitoring of ecosystems. The research will integrate novel lightweight perception modalities for robust perching in the wild, agile control algorithms
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PhD opportunity. Challenge: Design a just-in-time authorization and usage control stack that reacts to dynamic risk signals while guaranteeing legality, privacy, and ethical safeguards across
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application of scientific knowledge. Experience with quantitative research methods and data analysis is required. Experience with using LLMs is a plus. Excellent written and spoken command of English (C2
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you will uncover how nanoconfinement controls energy and electron transfer processes by using state-of-the-art time-resolved spectroscopy. The position is open to curiosity-driven, committed applicants
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investigate deep learning methods for local data augmentation and adaptive point density control, addressing the anisotropy and uneven sampling typical of urban LiDAR. You will work on a four-year doctoral