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of the distinctions, limitations, and appropriate applications of formal ontologies versus data dictionaries, vocabularies, and taxonomies. Essential Interview / Application Experience with semantic web technologies
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Intelligence (LLMs, RAG) and Cloud services. Functions to be developed: Assist in optimizing data ingestion, preparing datasets, and designing vector database structures/bases for semantic queries. Design
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background in Semantic Web technologies (RDF, OWL, SPARQL, SHACL) and ontology engineering methodologies and tools (e.g., Protégé). - Experience with Knowledge Graph architectures, graph databases
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natural language processing (NLP), knowledge graphs, and semantic search technologies. 2. Design, implement, and optimize secure, scalable, and high-performance AI solutions across multi-cloud
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of cloud and AI resources. Data Modeling, Warehousing & Integration (20%) Maintain relational and dimensional data models, including star schemas, facts, dimensions, semantic structures, and warehouse
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at least one of the following (Autonomic Intelligent Systems, Data Mining, Machine Learning, Semantic Web, Software Development, Web & Network Services) To apply for this role, please click on the 'Apply
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pipelines for processing large-scale structured and unstructured datasets using machine learning, natural language processing, semantic search, and knowledge graph technologies. 2. Design, implement, optimize
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, and other enterprise analytics platforms. The incumbent develops trusted data products, enterprise semantic models, dashboards, AI-enabled analytics solutions, and self-service reporting capabilities
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Kuratierung datenbezogener Organisations- und Vorhaltungsstrukturen und deren Verbindung zu bereits bestehenden Datenhaltungssystemen und Dateninfrastrukturen Semantic Web & Wissensgraphen: Entwicklung und
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). Desirable Interview / Application Familiarity with semantic web technologies (RDF, SPARQL), linked data principles, or large language model (LLM) APIs and agentic frameworks for automated data integration