Master Thesis: Cross-Lingual Safety and System Identity Alignment via DPO

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.

Open-weight multilingual models should maintain a consistent system identity and appropriate safety behaviour across languages.

However, safety alignment can vary across languages and may introduce over-refusal, particularly in lower-resource settings.

We have developed the openeurollm-model-identity dataset and a multilingual safety refusal suite. This thesis investigates

DPO-based alignment in Swedish and Icelandic, focusing on three questions:

• Data analysis: Assess the quality and suitability of the preference data, including both preference correctness and the

absolute quality of chosen responses.

• Implementation: Fine-tune Prelude 9B using identity and safety preference data with Hugging Face TRL or Megatron-LM. Compare

identity-only, safety-only, and combined training where feasible.

• Evaluation: Compare the resulting models against the baseline on identity consistency, safety and jailbreak robustness,

over-refusal on benign queries, and general instruction-following performance in Swedish and Icelandic.

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.

• 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.

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

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

or manning companies.