Researcher in Semantic Interoperability and Explainable, -driven AI Smart Systems

Job description

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 SAREF4

XAI 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 building 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.

• MSc degree in Computer Science, Information Systems, Industrial Engineering, Data Science, or a closely related field
• Familiarity with knowledge graphs, ontologies, and related semantic technologies, including RDF, SPARQL, and formal logic
• Familiarity with (neuro‑symbolic) machine learning methods and their application
• Analytical and conceptual thinking skills to work across technical and organisational perspectives
• Programming skills (e.g. Python, Java, or similar)
• Interest in interdisciplinary applied research

• You will be appointed part‑time for a period of 12 months for 0.4 FTE within a very stimulating and exciting scientific environment.
• Your salary and associated conditions are in accordance with the collective labour agreement for Dutch universities (CAONU).
• You will receive a gross monthly salary ranging from € 3 546,00 (step
  0) to € 4 241,00 (step
  4), depending on experience and qualifications.
• There are excellent benefits, including a holiday allowance of 8 % of the gross annual salary, an end‑of‑year bonus of 8.3 %, and a solid pension scheme.
• The flexibility to work (partially) from home.
• A minimum of 232 leave hours in case of full‑time employment based on a formal workweek of 38 hours. A full‑time employment in practice means 40 hours a week, therefore resulting in 96 extra leave hours on an annual basis.
• Free access to sports facilities on campus.
• A family‑friendly institution that offers parental leave (both paid and unpaid).
• Excellent support for research and facilities for professional and personal development.

Are you interested in this position? Please send your application via the 'Apply now' button below before September 7, 2026, and include:

• A Curriculum Vitae, including the names of two possible references, a list of publications, and a list of all courses attended and grades obtained.
• A cover letter (maximum 2 pages A4), emphasising your specific interest, qualifications, and motivations to apply for this position.

For more information regarding this position, you are welcome to contact Dr. João Rebelo Moreira (j.luizrebelomoreira).

Screening is part of the selection process.