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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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, Ontology engineering, Computational geoscience, or a related field; a strong interest in conceptual modeling, semantic modelling, ontology engineering, formal logics or cognitive modelling, and some
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; develop a hybrid question parsing pipeline using NLP and formal semantic representations; investigate Large Language Models (LLMs) as well as symbolic AI for question parsing; evaluate models based on a
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for adapting large language models to tasks mixing text and structured data, such as statistical report generation and semantic search across historical statistics publications. Successful PhD candidates will