AI-Powered RAG System for Research Data Management (6-12 months internship)

📢 Project Overview

We are offering an internship opportunity for a motivated student to contribute to the design and development of an AI-assisted counseling system for Research Data Management (RDM). The project focuses on developing a Retrieval-Augmented Generation (RAG) system to support scientists, data stewards, and other research staff by providing guidance on managing research data. The system will help users answer questions about how to collect, handle, store, and share research data in compliance with institutional policies, legal requirements, and best practices.

As part of the project, the intern will contribute to the development of a system that:
•    Assists users in understanding data management, sharing policies, and access constraints;
•    Provides clear, counseling-style explanations to support decisions regarding data access and sharing;
•    Delivers accurate, transparent, and reproducible information based on institutional knowledge and policies.

This internship is suitable for students in Computer Science, Artificial Intelligence, Data Science, Biomedical Engineering, or related fields.

🎓 Required & Preferred Skills

Required:
•    Enrollment in a Bachelor’s or Master’s program in a relevant field
•    Solid Python programming skills
•    Basic understanding of APIs and software architecture
•    Strong motivation to work on AI or data-driven systems

Preferred:
•    Knowledge of NLP, LLMs, or retrieval-based systems
•    Familiarity with machine learning concepts and NLP tooling for RAG systems
•    Experience with vector search or information retrieval systems
•    Docker and containerized deployment
•    Git-based version control and collaborative development tools
•    Understanding of data privacy or ethical AI considerations
•    Interest in scientific data or biomedical applications

📍 Practical Information

•    Duration: 6–12 months (flexible depending on study program)
•    Start Date: Flexible
•    Format: Hybrid or remote (subject to agreement)
•    Level: Bachelor’s or Master’s students

📬 How to Apply

Interested students should submit:
•    Curriculum Vitae (CV)
•    Short motivation letter (academic interests and relevance to this project)
•    Optional: GitHub profile or examples of previous work

Contact: l.morgado@nin.knaw.nl