Researcher in Semantic Interoperability and Explainable, Knowledge-driven AI for Smart Systems (SAREF4XAI)

Descrizione

Researcher in Semantic Interoperability and Explainable, Knowledge-driven AI for Smart Systems (SAREF4XAI) Sintesi in italiano (traduzione automatica): L'Università di Twente e la Vrije Universiteit Amsterdam offrono una posizione di ricerca part-time di un anno nel progetto SAREF4XAI, focalizzata sull'interoperabilità semantica e sull'IA spiegabile per ambienti intelligenti. Il ricercatore esplorerà come combinare ontologie, grafi di conoscenza e IA basata su conoscenza per supportare applicazioni interoperabili nel settore degli edifici intelligenti. Le mansioni principali includono la valutazione di approcci concettuali e tecnici per migliorare l'interpretabilità delle applicazioni AI in sistemi socio-tecnici complessi. È richiesta una laurea in Informatica o un campo correlato, e il ruolo prevede una stretta collaborazione con gruppi di ricerca e stakeholder esterni, contribuendo a un'agenda di ricerca più ampia sull'IA affidabile e trasparente. The rising popularity of AI-powered smart environments for building automation and management raises concerns about interoperability, transparency, and trust. These environments often operate across heterogeneous domains, rely on various standards and protocols, and are tailored to different tasks. This makes it difficult to guarantee meaningful integration, explainable decision-making, and automated task execution. Combining semantic technologies with knowledge-driven AI can help alleviate many of these challenges. Within the SAREF4XAI project, jointly conducted by the Semantics, Cybersecurity and Services (SCS) group at the University of Twente and the User-Centric Data Science (UCDS) group at Vrije Universiteit Amsterdam, we offer a 1-year, part-time research position focusing on semantic interoperability and explainable, knowledge-driven AI for smart environments. The researcher will explore how ontologies, knowledge graphs, and knowledge-driven AI can be combined to support interoperable and explainable applications in the smart buildings domain. Building on open modelling standards and recent breakthroughs in (neuro-) symbolic learning methods, the researcher will investigate and evaluate 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 collaboration between both research groups, engagement with external stakeholders, and participation in joint research activities and dissemination efforts, thereby contributing to a broader research agenda on semantic interoperability and explainable, knowledge-driven AI. This project aims to lay the foundation for long-term collaboration and future funding opportunities in the area of trustworthy, transparent, and interoperable AI for smart societies. Annuncio in inglese. Fonte: Euraxess (Commissione europea).