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 an ambitious Master’s thesis student to join us at AI Sweden to work alongside other industrial partners (Zenseact, Scaleout, Ekkono AI) on a unique mix of AI research topics for a use case in automotive.

Background

“As the automotive industry continues work on AI-driven automation, ensuring that data can be leveraged for safer driving without compromising privacy or security is paramount. This project pioneers a new approach to real-time federated learning, enabling continuous AI adaptation while fully complying with increasing regulations. By tackling these challenges, we are not just advancing autonomous driving—we are shaping the future of safe and secure, intelligent mobility."

- Jonas Ekmark, Head of New Technology, Zenseact

The increasing use of edge devices in vehicles (sensors such as LiDAR, cameras, etc.) has led to massive data generation, creating challenges for real-time AI adaptation, data privacy, and security. Traditional Federated Learning (FL) methods are unsuitable for real-time data-streaming environments like autonomous vehicles with high requirements on privacy and security. This project aims to advance safe automated vehicles by enhancing AI-driven perception, situational awareness, and decision-making while mitigating privacy breaches and national security risks. We introduce continuous, active federated learning for data streams to equip FL to work with high amounts of constantly incoming real-world data at the edge. The results will inform regulatory discussions on AI-driven traffic safety, supporting the development of policies for secure, automated vehicles. Our proposed approach enables data-efficient continuous model adaptation without requiring stored data, reducing cybersecurity risks and misuse of collected images or sensitive location data. The overall project, in which the student will be developing their Masters thesis, is a joint collaboration between AI Sweden (coordinator), Zenseact (autonomous driving use case), Scaleout Systems (FL frameworks), and Ekkono Solutions (edge learning). By integrating industry-leading expertise in these fields, the project ensures mission-critical innovation for transportation systems.

The project

The work in this project is structured across the following thematic pillars and areas of research. A thesis may focus on one of the following topics or combine several of them. The exact research question and methods will be decided together with the student, depending on the student's background and interests, previous work, and practical feasibility.

• Single-Pass Active Learning: how can we design a data filter to judge the usefulness of incoming data for a given model state, under the assumption of transient data, i.e. data cannot be hoarded and learned from on-demand?

• Self-Supervised Learning for Data Streams: how can we accommodate the real-life condition of not having labels on demand at the edge? In particular, how does the SSL approach influence the type of active learning we can do?

• Managing catastrophic forgetting: what mitigations can we put in place in terms of the learning modality (self-supervised learning approach) and active learning filter to identify and mitigate catastrophic forgetting?

• Federated learning for data streams: how can our use of federated learning as a means for data privacy be optimized in light of the fact that data is fed to the system as streams of transient data? What implications does the federation strategy have for the learning approach itself (self-supervised learning algorithm and active learning approach)?

Fitting these puzzle pieces together is not a trivial or straightforward task, and requires formal investigation that is highly relevant for future technology development and production plans for autonomous driving.

What you would do

Projects can be theoretical, computational, or a combination of the two. The main goal is to use a simplified model or controlled experiment to understand the reasoning phenomenon: Students will design controlled experiments, run them on open-weight reasoning models, and measure carefully. You will have access to substantial compute, and you will be working on questions that are genuinely open, in a frontier area of AI research — there is a real chance your results are the first anyone has.

Who we’re looking for

We are seeking a curious, independent, and self-driven MSc student who wants to work at the absolute frontier of AI research.

• Ongoing Master’s studies in Computer Science, Data Science, Engineering Physics, Complex Adaptive Systems, Machine Learning, or similar.

• You should be comfortable with Python and deep learning, and comfortable with the idea that an experiment might tell you something you did not expect.

• Prior experience with active learning, continuous / incremental learning, self-supervised learning is useful but not required; an interest to pursue these topics is.

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, change leaders, journalists, linguists, policy professionals and entrepreneurs—all working with a higher purpose in mind than “just” tech. 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. We offer our team members a place to grow, an environment for personal development and achievements.

Practical details

This Master’s thesis will be carried out at AI Sweden at Lindholmen Science Park, Gothenburg. We aim for a hybrid working mode, with some time spent on-site, but there is room for flexibility.

Application Deadline: 2026-12-27 (rolling selection – position may be filled earlier).

Start Date: January 2027

Contact

Mauricio Muñoz, Senior Research Engineer and Project Lead at AI Sweden

Send a short note about what interests you, a CV and available transcripts. Questions welcome before you apply.

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