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Generative procedural urban modeling and rule induction Semantic enrichment and automated feature extraction Reconstruction or inference of structural detail from sparse spatial inputs Integration
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synthesis and procedural world-building AI-assisted asset creation and semantic scene enrichment Synthetic data generation for perception and simulation Embedding real-time inference within rendering
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semantic heterogeneity—differences in terminology, structure, and logic—remains a central barrier to reusability, interoperability, and reproducibility. This postdoctoral position addresses a fundamental and
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Semantic Technologies. The candidate is expected to strengthen the capacity of research and supervision of the group in knowledge representation, in particular, logics for multi-agent systems. Many
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Familiarity with retrieval-augmented generation (RAG) and knowledge graph applications Experience with semantic search, ontology development, and cross-domain data integration Background in clinical safety
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) 2 selected publications (links to publicly open URLs) Home page URL Google Scholar or Semantic Scholar URL (optional) Name of potential faculty mentors at Cornell (max 2 names; optional) 2 letters