Master Thesis: Cross-Lingual Safety Alignment & System Identity Injection 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.

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

Project Background and Problem Statement

T4.6 has established the openeurollm-model-identity dataset and a multilingual safety refusal suite. This thesis asks:

• Can joint DPO co-training on openeurollm-model-identity and Safe-RLHF pairs eliminate cross-lingual safety leaks and over-refusal without degrading instruction-following performance in Swedish and Icelandic?

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.

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

Amaru Cuba Gyllensten, Senior Research Scientist

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] OpenEuroLLM Consortium, "openeurollm-model-identity & Safety Evaluation Suite," 2026.

[2] PKU Alignment Team, "Safe-RLHF: Safe Reinforcement Learning from Human Feedback," 2024