About the position

You are expected to contribute to the development of internationally visible, foundational research in AI-driven semantic structure extraction, automated reasoning-flow modeling, and adaptive content generation. The research focuses on methods for analyzing and representing deep semantic and pedagogical structures in scientific and educational materials; high-fidelity extraction of conceptual and reasoning blocks; inference-time rationale generation; and adaptive, learner-aware sequencing of content. This includes work on semantic parsing, structured NLP, graph-based neural models, metacognitive prompting, ontology alignment across disciplines, and human-in-the-loop optimization.

In this context, interdisciplinary research is strongly encouraged—particularly collaborations spanning computer science, computational linguistics, cognitive science, and the learning sciences. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve system interpretability and instructional quality. The successful candidate will also contribute to high-quality publications, release research prototypes, and support demonstrator systems that deliver structured semantic extraction, rationale-aware content generation, and cross-domain transfer of reasoning structures.

Additionally, the successful candidate is expected to support teaching activities in areas such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in course delivery, advising students, supervising Bachelor/Master theses, and engaging in methodological innovation for online, hybrid, and in-person learning environments. The university provides strong support for early-career researchers, including mentorship, administrative assistance, access to computational resources, conference funding, and opportunities to collaborate with other research groups and industrial partners working at the intersection of AI and digital education.

Mandatory requirements

• Master’s or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a related field.
• Strong research interest and practical experience in one or more of the following areas:
  • semantic parsing and structured representation learning
  • knowledge graphs or graph-based reasoning
  • transformer models, sequence modeling, or GNNs
  • natural language generation and explainability
  • educational AI, personalization algorithms, or cognitive modeling
• Evidence of research potential through publications, a strong thesis, or significant projects.
• Experience or interest in innovative teaching and learning approaches.
• Ability to translate theoretical insights into engineered prototypes and systems supporting scientific or educational use cases.
• Responsible, self-motivated, and capable of working independently as well as collaboratively.
• Excellent verbal and written communication skills.
• Intercultural competence and experience in international environments.
• Fluency with co-pilot tools for coding and writing.
• Fluency in English, the language of instruction and communication on campus.

Funding & Appointment Terms

The appointment provides full financial coverage through a dedicated fellowship, comprising:

• Monthly stipend of €1,650
• Monthly research-cost allowance of €100 (Forschungskostenpauschale)
• Health-insurance subsidy of €100 per month
• Supplementary €550 mini-job allowance to support parallel part-time employment (optional)

Application Details

• Expected start date: September, 2026

Application package must include: 

• Curriculum Vitae (CV);
• Academic transcripts ;
• A detailed letter of motivation outlining research interests and career goals;
• 2 recommendation letters;

Applications to be reviewed on a rolling basis. Shortlisted candidates will be invited to interviews.