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, semantic modelling, ontology engineering, formal logics or cognitive modelling, and some experience with linked data standards and knowledge graph tools (e.g., RDF, OWL, SHACL); familiarity with or interest
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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 gold standard of geo
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-driven taxonomy; 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
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of the following: static and/or dynamic program analysis, programming language techniques (such as semantics, type systems, runtime systems, etc), formal verification, or software engineering
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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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, Computer science, Ontology engineering, Computational geoscience, AI and NLP, or a related field; a strong interest in conceptual modelling, semantic modelling, ontology engineering, formal logics or cognitive
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for logical foundations, a new linguistic layer representing the intermediate steps between natural language texts and formal proof documents, and the automatisation tools for the efficient construction