As Sweden's national center for applied AI, we're on a mission to accelerate the use of AI to benefit our society, our competitiveness, and everyone living in Sweden. We drive impactful initiatives in areas such as healthcare, energy, and public services while pushing the boundaries of AI research in fields such as natural language processing, machine learning and AI security. Join us in harnessing the untapped value of AI to drive innovation and create sustainable value for Sweden.

We are now looking for a master thesis student to join our team.

Introduction

Automated evaluation is vital for tracking LLM post-training progress. "LLM-as-a-judge" paradigms are highly scalable but often exhibit severe biases, particularly in non-English languages where they may conflate grammatical fluency with factual adequacy, heavily penalizing minor linguistic errors.

Project Background and Problem Statement

In WP5, OpenEuroLLM is relying on the JudgeArena framework. For Swedish, we face the challenge that English-centric judge models misjudge local knowledge or nuanced fluency. This thesis explores whether providing an LLM judge with few-shot, human-calibrated Swedish examples can effectively decouple fluency from adequacy, aligning the automated Elo scores with human expert ratings.

Outline

The goal is to refine and validate JudgeArena for Swedish post-training evaluation.

• Literature study: Review automated evaluation paradigms, LLM-as-a-judge biases, and metrics distinguishing fluency from adequacy.

• Implementation: Annotate a high-quality Swedish dataset with separate scores for fluency and adequacy. Use these as calibration prompts for the Judge model in JudgeArena.

• Evaluation: Correlate the calibrated JudgeArena outputs against human judgments on a holdout set of Prelude 9B's generated responses, quantifying the improvement in Pearson/Spearman correlation.

Who we’re looking for

We are seeking curious, self-driven MSc students eager to work at the frontier of open-weight European AI research (LLMs). You thrive on empirical discovery, design rigorous experiments, and let data challenge your assumptions.

• Ongoing Master’s studies in Computer Science, Data Science, Machine Learning, Engineering Physics, or a related quantitative field.

• Proficiency in Python and hands-on experience with modern deep learning frameworks (PyTorch, Hugging Face ecosystem).

• Familiarity with LLM post-training alignment (e.g., SFT, DPO, RLHF/RLVR) or context-extension, alongside comfort running distributed GPU training in Linux/HPC environments.

At AI Sweden, we are committed to building diverse and inclusive teams. Some positions may be subject to export control regulations, which means that specific requirements may apply.

Why should you do your thesis with AI Sweden?

Doing your thesis at AI Sweden means working alongside leading AI scientists and change leaders. AI Sweden is Sweden’s National Center for AI, we drive research questions that have both a long shelf-life and are widely applicable to Swedish industry and the public sector. We aim for publications at the most competitive venues and celebrate a culture of research excellence.

As an organization, we’re uniquely positioned at the sweet spot of governmental influence and startup agility. Small enough to stay adaptive and have fun but backed by and in close contact with both the government, academia and private and public sector.

Practical details
Location: Hybrid (Gothenburg / Stockholm) or Remote.

Application Deadline: 2026-11-20 (rolling selection – position may be filled earlier).
Start Date: January 2027

Contact

If you have any questions or thoughts, don’t hesitate to contact:

Anna Lokrantz, AI Engineer

Danila Petrelli, Senior Data Lead/Research Scientist

AI Sweden does not accept unsolicited support and kindly ask not to be contacted by any advertisement agents, recruitment agencies or manning companies.

References

[1] Zheng, L., et al., "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena," NeurIPS 2023.

[2] OpenEuroLLM WP5, "JudgeArena Framework," 2026.