Postdoc In AI-Driven Adaptive Learning Systems (CLARA project)

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.

Job Description

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.

A central technical challenge guides this position:

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?

Research tasks and key deliverables

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 containerized 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.

Job Requirements

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, HuggingFace Transformers, LangChain).
• 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 and communication skills in English.
• Ability to work effectively in an interdisciplinary team, including colleagues from social and educational sciences.

Desirable qualifications

The following qualities are not required but will significantly strengthen an application:

• Experience working with or conducting research in educational settings — hands-on knowledge of how learning environments operate greatly facilitates collaboration with the pedagogical team and the PhD candidate.
• Familiarity with multimodal data (audio, video, interaction logs) and time-series analysis of social interaction.
• Experience with or interest in agentic AI systems and real-time inference pipelines.

Awareness of Challenge-Based Learning or comparable active learning frameworks.

Conditions of Employment

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• A postdoctoral appointment for 0.8 FTE (32 hours/week) for a period of 2.5 years.
• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 10 (min. € 4,241 max. € 5,538 gross per month).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs on general skills, didactics and topics related to research and valorization.
• Supervision and mentoring by an experienced interdisciplinary team spanning engineering, innovation sciences, and educational research.
• Active collaboration partners at the University of Twente and Maastricht University, plus involvement in the Serpentine student co-design initiative and the broader CLARA consortium.
• Opportunities to publish in leading venues and to present at international conferences.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate.
• Partially paid parental leave and an allowance for commuting, working from home and internet costs. 
• A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
• A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.

On our website you can discover even more information about our conditions of employment. Build on your career at TU/e!

About us

We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home to over 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community.

Our university is located in Brainport Eindhoven — a world‑leading tech region with more than 7,000 high‑tech companies and strong R&D activity. Known for breakthroughs in AI, photonics, semiconductors and advanced manufacturing, Brainport is a place where technology serves people and society. Learn more about the Brainport region here.

This position is hosted by the Department of Industrial Engineering & Innovation Sciences (IE&IS), which combines engineering, social sciences, and innovation studies to address complex societal and technological challenges.

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager:

• Prof. dr. Bert Sadowski — Associate Professor, Department of IE&IS, TU/e — [email protected]
• Prof. dr. Karolina Doulougeri — Assistant Professor, School of Education, TU/e — [email protected]

Visit our website for more information about the application process. You can also contact HR Services, [email protected].

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application

We invite you to submit a complete application using the apply-button. The application should include:

• Cover letter (max. 1 page) describing your motivation and qualifications for the position.
• Curriculum vitae, including the contact details of two or three references. We may reach out to references at any stage of the recruitment process; please notify your references upon submitting your application.
• One representative publication or code portfolio demonstrating relevant technical work.
• Research and technical challenge response (see below).

Technical challenge (required)

As part of the application, candidates are asked to complete a short technical challenge designed to give a concrete and objective view of their skills and thinking. It has two parts:

Part A — Implementation (approx. 1–3 hours). Suppose you have access to a data stream from an upstream pipeline that classifies students' socio-emotional and socio-cognitive states (e.g., confusion, frustration, disengagement, neutral) based on their speech. Write a short Python script or notebook that (1) processes a sample of this data (you may use a small synthetic or publicly available dataset), (2) applies a rule-based or model-based method to identify a moment where an AI agent's support intervention would be warranted (a 'trigger event'), and (3) generates or selects an appropriate scaffolding message for the group. Include brief comments explaining your design choices.

Part B — Reflection (max. 400 words). Briefly discuss: What kinds of support would be appropriate for a group in this situation, and what would be inappropriate or potentially harmful? What are the key limitations of using speech emotion data for this purpose, and how would you address them in a responsible deployment?

There is no single correct answer. We are looking for clear reasoning, technical competence, and awareness of the educational and ethical dimensions of the task. Submissions may be in the form of a GitHub link, Jupyter notebook, or a PDF.

Deadlines and start date

• Deadline for applications: 17 August 2026
• Interviews: Interviews will be held on 14 September 2026. Selected applicants will be notified by 7 September 2026 at the latest.
• Expected starting date: 1 November 2026

Please note

• You can apply online. We will not process applications sent by email and/or post. 
• A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check, please consult the National Knowledge Security Guidelines.
• Please do not contact us for unsolicited services.