Postdoc In AI-Driven Adaptive Learning Systems; CLARA project

Departments, Department of Industrial Engineering & Innovation Sciences

Are you fascinated by how AI agents can support students' collaborative learning? Join the NRO-funded CLARA project and design an LLM-based social agent that scaffolds group work in Challenge-Based Learning at TU/e.

Eindhoven University of Technology invites applications for a postdoctoral research position within the recently NRO-funded project AI as a Social Agent to Support Group Learning Processes (CLARA). This interdisciplinary project investigates how AI — specifically large language model (LLM)-based agents — can act as adaptive social agents to support students' collaborative learning in Challenge-Based Learning (CBL) environments.

You will be embedded in the Department of Industrial Engineering & Innovation Sciences (IE&IS) and form the technical core of the CLARA consortium, which brings together researchers from TU/e, the University of Twente, and Maastricht University. Working closely with a PhD candidate and the project's supervisory team, you will design, train, and iteratively refine the CLARA AI agent — bridging cutting‑edge machine learning methods with empirical insights from the educational arm of the project.

How can an LLM-based AI social agent be designed, fine‑tuned, and deployed to detect socio‑cognitive and socio‑emotional triggers in student group work, and deliver contextually appropriate scaffolding in real time?

Rather than following a fixed phase sequence, you are expected to make substantive contributions across the following six areas throughout the appointment:

• Trigger detection system. Design and implement an NLP/LLM-based system capable of identifying socio‑cognitive and socio‑emotional triggers in student group interaction data (text, audio, and multimodal streams), drawing on the HASRL framework and the empirical taxonomy developed by the PhD candidate.
• Model training and fine‑tuning. Fine‑tune large language models on annotated educational datasets collected during the project, ensuring the agent's responses are pedagogically valid, contextually appropriate, and consistent with collaborative learning theory.
• Scaffolding mechanism design. In close collaboration with the PhD candidate and educational supervisors, develop and evaluate adaptive scaffolding strategies that the AI agent delivers as interventions, refining them iteratively based on classroom data and pedagogical feedback.
• Classroom implementation and evaluation. Support and co‑lead pilot studies in real CBL classrooms; contribute to data collection, analysis, and interpretation of the agent's performance, attending to both technical metrics and educational outcomes.
• Responsible AI and fairness auditing. Conduct algorithmic fairness validation of the CLARA system, develop documentation on data governance and GDPR compliance, and contribute to the project's open‑science outputs, including containerised model workflows.
• Dissemination and scientific output. Publish findings in peer‑reviewed journals, present at leading conferences, and contribute to practice‑oriented outputs and knowledge transfer activities within the NRO consortium.

We are looking for a technically strong and intellectually curious researcher. A rigorous computational background is essential; experience with educational contexts is valuable but secondary to technical excellence.

Required qualifications

• A PhD degree in Computer Science, Artificial Intelligence, or a closely related technical field.
• Demonstrated expertise in large language models (LLMs), natural language processing (NLP), and/or machine learning, with a verifiable track record (e.g., publications, thesis, or open‑source contributions).
• Proficiency in Python and relevant ML frameworks (e.g., PyTorch, Hugging Face Transformers, Lang Chain).
• Experience with model fine‑tuning, prompt engineering, retrieval‑augmented generation (RAG), and with agentic AI and AI agents.
• Familiarity with responsible AI principles, including fairness, transparency, and data governance.
• Proven experience with supercomputing / HPC environments.
• Strong academic writing…