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Analysis of vulnerabilities in low-resource language settings, and the effect of typological diversity on the security landscape in multilingual settings Formal semantic or symbolic methods for monitoring
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settings Formal semantic or symbolic methods for monitoring, evaluating, and improving LLM robustness. The positions are embedded in an active and rapidly growing research environment, including ongoing
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-water settings. The research will develop a unified framework that fuses heterogeneous sensing modalities through uncertainty-aware probabilistic optimization while maintaining semantic, structural, and
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providing mechanisms to manage interoperability and semantic challenges when working with heterogeneous healthcare datasets. To ensure practical applicability, the architecture will be evaluated using
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— localization and mapping (e.g., SLAM), motion planning, and semantic perception — focusing on multimodal sensor data fusion (LiDAR, RGB-D, IMUs) for robust real-world performance. Research areas include