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& Interoperability – and more specifically, in the following tasks: Task 4.1: Ontology Definition, Alignment and Formalization (CIDOC-CRM & INSPIRE), focusing on the design and extension of the MNEME semantic model
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document and differentiate the use of semantic technologies from primitive data dictionaries and taxonomies through to formal ontologies and logic across the project's infrastructure, ensuring the right tool
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conceptual and technical approaches for leveraging semantics and formal background knowledge to enhance explainability in downstream AI tasks for complex socio-technical systems. The position involves close
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Postdoctoral Researcher in Semantic Knowledge Engineering & Interoperability in Materials Science in
Bundesanstalt für Materialforschung und -prüfung (BAM) | Berlin, Berlin | Germany | about 2 months agotranslated into formal knowledge representations. These form the basis for the development and integration of semantic models into ontologies and knowledge graphs as a semantic layer of federated data
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contested environments, leveraging Vision-Language-Action (VLA) models, multi-sensor fusion (LiDAR, radar, cameras, IMU), semantic SLAM, and EW-resilient mission planning. The platform will feature modular
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Architecture Development: Contribute to the specification and development of an Edge Computing architecture to enable edge AI and distributed learning. - Semantic Middleware Integration: Participate in
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, multidimensional weakness and failure taxonomies, and vulnerability models define the lexis, syntax, and semantics of the BF formal language and form the basis for the definition of secure coding principles. The BF
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, (geo)data infrastructures, and (geo)data curation. data storage, linked data, open science practices, and web interface design; Knowledge of, or interest in, formal languages, semantic modelling
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semantic representations. Develop methods to assess AI-generated hypotheses for plausibility, novelty, evidence support, contradiction, uncertainty and explainability. Publish in leading NLP, AI and
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formal ontologies supporting interoperable research data infrastructures, establishing data and repository policies, and managing the content and structure of data repositories in close collaboration with