Postdoctoral Researcher – LLMs for AI-Assisted Educational Support

Vrije Universiteit Amsterdam (VU); Netherlands; Hybrid; EUR 41,000 - 64,000; Full time

Benefits offered by this job

Hybrid work

Holiday allowance 8%

End-of-year bonus 8.3%

Commuting allowance

Job summary

Vrije Universiteit Amsterdam (VU) invites applications for a Postdoctoral Researcher to advance LLM components supporting OPP workflows in education. You will build prompting strategies, structured inputs, and evaluation rubrics while collaborating with researchers and developers.

The role emphasizes human-centered AI, with a focus on integration into Presentis SIS and user studies with educators. A PhD and strong NLP/LLM background are required, plus English fluency and willingness to learn

Qualifications

• PhD in AI, CS, Computational Linguistics, NLP, HAI or related field.
• Ability to work in a multidisciplinary team with researchers, software developers and educators.
• Excellent English; Dutch proficiency is strongly preferred.
• Affinity with education or educational technology is desirable.
• Hands-on experience with LLMs and modern NLP methods.
• Good programming skills (Python) and experience with ML/NLP frameworks.

Responsibilities

• Select and benchmark LLMs for Dutch-language OPP tasks.
• Develop and evaluate LLM components for generating specific OPP sections.
• Design prompting strategies and structured input representations.
• Investigate when prompt engineering suffices vs fine-tuning or RAG.
• Develop rubric-based methods to detect quality issues and propose improvements.
• Collaborate with software developers on AI integration into Presentis SIS.
• Co-design and run user studies with teachers and professionals.

Skills

NLP with LLMs

Python programming

Prompt engineering

Research methodology

English proficiency

Multidisciplinary collaboration

Education

PhD in AI/CS/Computational Linguistics/ NLP

Tools

PyTorch

Transformers

Job description

Offer Description

As a Postdoctoral Researcher, you will play a central role in the development of Large Language Model (LLM) components that support education professionals in creating, maintaining and improving Individual Development Perspective Plans (Ontwikkelingsperspectiefplannen; OPPs).

The project does not aim to automate the creation of complete OPPs. Instead, you will investigate how LLMs can best support education professionals with specific, well-defined subtasks within the OPP workflow. Examples include generating a first draft of the Integrative Student Profile (Integratief Beeld) based on already completed OPP sections, supporting the formulation of the Educational Perspective / justification of the expected educational pathway, and providing targeted feedback on the quality of existing OPP text.

A key research challenge is to determine how information from different OPP sections (e.g., support needs) can be represented and provided to an LLM so that the generated output is coherent, concrete, actionable and consistent with the underlying information. You will develop and evaluate prompting strategies, structured input representations and, where useful, fine-tuned models for these tasks.

You will also work on LLM-based quality feedback. A quality rubric developed in the first phase of the project defines criteria such as concreteness, actionability, coherence, specificity of support needs and quality of the integrative profile. You will investigate how LLMs can use these criteria to identify weaknesses in OPP text and provide concise, prioritized and practically useful suggestions for improvement.

An equally important part of the position concerns the interaction between the AI component and the education professional. The AI system should support professional judgement rather than replace it. You will therefore investigate questions such as: When should AI support be offered? What information should be shown to the user? How should generated text and feedback be presented? How can users easily accept, reject or adapt suggestions? And how can the system make clear which OPP information was used to generate a suggestion?

Together with the software development partner, you will help design how these AI components can be integrated into an existing student information system. This includes defining the information exchanged between the student information system and the LLM, designing modular API-based components, and contributing to user-interface concepts that allow education professionals to invoke AI support within their normal workflow.

The research will be conducted iteratively and in close collaboration with education professionals. Prototype components will first be evaluated independently for output quality and will subsequently be integrated into the Presentis student information system (leerlingvolgsysteem) for user-centred evaluation and refinement.

To get a better idea about the project, see: https://www.ru.nl/onderzoek/onderzoeksprojecten/opp-vullen-en-onderhouden-met-ai

Your duties

• select and benchmark state-of-the-art open-source and commercial LLMs for Dutch-language OPP-related tasks
• develop and evaluate LLM components for generating specific OPP sections, initially focusing on the Integrative Student Profile and the Educational Perspective / justification of educational pathway
• develop prompting strategies and structured input representations that combine information from relevant OPP sections
• investigate when prompt engineering is sufficient and when techniques such as fine-tuning or retrieval-augmented generation provide added value
• develop rubric-based LLM methods for detecting quality issues in existing OPP text and generating concise, prioritized improvement suggestions
• develop methods for identifying generic or baseline support that occurs repeatedly across OPPs and distinguish this from student-specific support needs
• design and evaluate human-AI interaction patterns for AI-assisted OPP writing and revision
• investigate how generated text and feedback can best be presented so that education professionals retain control over the final content
• collaborate with software developers who will develop the technical architecture and API-based integration of LLM components into the Presentis student information system
• define which OPP data elements should be transferred to an LLM component and how generated output should be returned and incorporated into the existing workflow
• evaluate LLM output using the OPP quality rubric developed in the project and through evaluation with education professionals
• conduct co-design and user studies with teachers, behavioural scientists, support coordinators and other education professionals
• analyze the quality, reliability and limitations of generated OPP text and AI feedback
• contribute to open-source implementations of reusable OPP-specific LLM components and evaluation tools
• publish results in peer-reviewed AI, NLP, Human-AI Interaction and/or Educational Technology venues
• supervise MSc students working on related research topics
• collaborate closely with project partners from education, research and software development

We are looking for a motivated researcher with expertise in modern Natural Language Processing and Large Language Models, who enjoys translating cutting‑edge AI research into practical educational applications.

Job requirements

• PhD in Artificial Intelligence, Computer Science, Computational Linguistics, Natural Language Processing, Human-AI Interaction, Data Science or a closely related field
• ability to work in a multidisciplinary consortium involving researchers, software developers and education professionals
• excellent communication skills in English; proficiency in Dutch, or the willingness and ability to develop sufficient Dutch proficiency to work with Dutch-language educational materials and users, is strongly preferred
• affinity with education, special education or educational technology is highly desirable
• demonstrated hands‑on experience with Large Language Models and modern NLP methods
• good programming skills, preferably in Python, and experience with relevant ML/NLP frameworks such as PyTorch and Hugging Face Transformers
• experience with prompt engineering and systematic evaluation of generative AI systems
• ability to design and run empirical comparisons between different models, prompting strategies and system configurations.
• experience with qualitative and/or quantitative evaluation of generated text
• interest in Human-AI Interaction and in designing AI systems that support rather than replace professional decision‑making
• experience with Retrieval‑Augmented Generation, model fine‑tuning, structured generation, LLM evaluation frameworks or human‑centred AI is an advantage
• experience with co‑design, user studies or iterative prototyping with domain professionals is an advantage.
• analytical and scientific writing skills

We offer the opportunity to work on a socially relevant AI project in close collaboration with schools, software developers and educational experts. The position provides ample opportunity to publish internationally while contributing directly to AI applications that can improve educational practice.

As a university, we strive for equal opportunities for all, recognising that diversity takes many forms. We believe that diversity in all its complexity is invaluable for the quality of our teaching, research and service. We are always looking for talent with diverse backgrounds and experiences. This also means that we are committed to creating an inclusive community so that we can use diversity as an asset.

We realise that each individual brings a unique set of skills, expertise and mindset. Therefore we are happy to invite anyone who recognises themselves in the profile to apply, even if you do not meet all the requirements.

Additional Information

A challenging position in a socially engaged organisation. At VU Amsterdam, you contribute to education, research and service for a better world. And that is valuable. So in return for your efforts, we offer you:

• a salary of minimum € 3.706,00 (Scale 10) and maximum € 5.760,00 (Scale 10) gross per month, on a full‑time basis. This is based on UFO profile Researcher 4. The exact salary depends on your education and experience
• a position for EITHER 1.0 fte for 15 months OR 0.8 fte for 19 months.

We also offer you attractive fringe benefits and regulations. Some examples:

• a full‑time 38‑hour working week comes with a holiday leave entitlement of 232 hours per year. If you choose to work 40 hours, you have 96 extra holiday leave hours on an annual basis. For part‑timers, this is calculated pro rata
• 8% holiday allowance and 8.3% end‑of‑year bonus
• contribution to commuting expenses
• hybrid working enables a good work‑life balance

Contact details: Prof. Dr. Koen Hindriks: [e-mail]