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Artificial intelligence is transforming society. AI Sweden is the national centre for applied artificial intelligence, jointly funded by the Swedish government and our partners, both public and private. Our mission is to accelerate the use of AI for the benefit of our society, our competitiveness, and for everyone living in Sweden. We are now looking for students to join the Unsupervised Track of AI Sweden's Master Thesis Program. Important: This track is for students who already have a thesis topic and supervisor. You will not be provided with a project from us. We are looking for independent, proactive students who want to leverage our community and resources to complete their work. About the Program The Unsupervised Track is designed for students who have defined their own thesis project. As a participant, you'll gain access to: * Our office and community: Use our space in Gothenburg or Stockholm and connect with a diverse network of professionals. * A collaborative environment: Benefit from a community of peers and experts by sharing experiences and tackling challenges together. * Exclusive events: Attend networking sessions and activities that can help you build your professional network. What we're looking for We believe the ideal candidate for this track is a person who: * Has a growth mindset, eager to expand their skills through real-world application. * Is proactive and takes ownership of their work. * Enjoys collaborating with a diverse community, sharing experiences and challenges. Who can take part? To take part in the Master Thesis Program, you must either be a student at an AI Sweden partner university, or write your thesis with an AI Sweden partner organization. Before you express your interest through this ad, check out the partner list to make sure you qualify to participate.  Our partner universities are at the moment Blekinge Institute of Technology, University of Borås, Chalmers University of Technology, University of Gothenburg, Stockholm School of Economics, Halmstad University, University West, Linköping University, Linnaeus University, Lund University, Malmö University, Swedish Defence University, Swedish University of Agricultural Sciences and Örebro University. Have you already decided on a thesis topic? Great! Make sure to state your project and any potential collaboration in your application. Collaborative project? If you are two students writing your thesis together, you can either submit one joint application or apply individually and mention your collaboration in both applications. Still looking for a topic? You can either submit an application and inform us about your topic later, or come back to complete your application once you have a topic in mind.  In order to comply with all applicable immigration and export control regulations, we are limiting this opportunity to citizens and permanent residents of EU, Norway, Switzerland, Iceland, India, the UK, Canada, USA, Mexico, Japan, and South Korea. Applications close at the beginning of January 2027. For any questions, please contact our Head of Talent Programs, Sofia Hedén on sofia.heden@ai.se.

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 For LLMs to serve effectively in public administration and enterprise workflows, they must accurately invoke external APIs and structured tools (Function Calling)[cite: 10, 14]. While SFT provides basic tool-use syntax, Reinforcement Learning with Verifiable Rewards (RLVR) is required to optimize multi-step agentic trajectories and error recovery. PROJECT BACKGROUND AND PROBLEM STATEMENT OpenEuroLLM is developing the oellm-rlvr framework to support agentic and function-calling rollouts. * This thesis asks: Does RLVR training on tool-execution environments (e.g., Tau-Bench, BFCL) significantly improve multi-step function-calling accuracy and schema adherence in Swedish and English compared to SFT alone? OUTLINE * Literature Study: Review agentic environment design, function-calling benchmarks (BFCL, Terminal-Bench), and RLVR mechanics. * Implementation: Adapt oellm-rlvr to execute tool-use rollouts with deterministic execution verifiers on Prelude 9B. * Evaluation: Benchmark tool-calling precision, schema validity, and Model FLOPs Utilization (MFU) across single-turn and multi-turn administrative workflow. 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: Birger Moëll, Senior Research Scientist Niclas Hertzberg, AI Engineer 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, "oellm-rlvr Agentic & Function Calling Environment," 2026. [2] Gorilla Team, "Berkeley Function Calling Leaderboard (BFCL)," 2024.

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 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. PROJECT BACKGROUND AND PROBLEM STATEMENT 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: 1. System identity: Does the model reliably identify itself as OpenEuroLLM and resist attempts to overwrite that identity? 2. Safety alignment: Does the model appropriately refuse genuinely unsafe requests across languages? 3. Over-refusal: Does safety alignment cause the model to refuse legitimate requests? The broader question is whether these behaviours can be improved without degrading general instruction-following performance. OUTLINE * Literature study: Review cross-lingual safety alignment, over-refusal, model identity, and preference optimization. * 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. 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: 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