Data, 3D Reconstruction & Generative AI: Master Thesis Projects 2027, Cluster B

Help build the foundations behind modern AI

What if your master’s thesis could help AI systems understand and learn from the real world more accurately?

At Zenseact, advanced AI depends on more than neural networks. It requires high-quality data, reliable sensors, realistic 3D environments, scalable annotation methods, and efficient tools for experimentation.

Cluster B brings together AI, computer vision, 3D reconstruction, generative models, sensor technology, and data engineering. You will work on a defined research challenge using relevant data and modern methods, contributing knowledge that may support safer and more capable driving technology.

You do not need to know the answer before you begin. What matters is your ability to investigate a problem thoughtfully, learn from evidence, and develop your understanding throughout the thesis.

Choose Your Project

B1: 3D Gaussian Splatting Reconstruction for Enhanced Parking

Explore how 3D Gaussian Splatting can reconstruct detailed driving environments and support enhanced parking applications.

B2: Data Curation with Self-Supervised Vision Foundation Models

Investigate how foundation models can organize and curate autonomous-driving data at scale, helping identify relevant scenes without relying entirely on manual labels.

B3: Multi-Camera LiDAR Calibration via 3D Gaussian Splatting

Develop and evaluate methods for jointly calibrating multiple cameras and LiDAR using a shared 3D representation.

B4: Weather Editing for Occupancy-Guided World Models

Explore how weather conditions can be modified in driving scenes while preserving the geometry needed for reliable world models and evaluation.

B5: Robust 3D Head Reconstruction for Digital Human Generation

Investigate methods for reconstructing human heads in 3D and generating realistic digital human representations.

B6: Diffusion-Based Image Synthesis from 3D Point Clouds (Location for this project: Lund, Sweden)

Explore how diffusion models can generate images from reconstructed 3D point clouds and how geometry can guide realistic image synthesis.

Read more about the projects here: https://zenseact.com/mtp_cluster-b/

Every Cluster B thesis combines research with practical experimentation. You will work with real-world data, develop and evaluate methods, analyze results, and contribute insights that may help shape future AI and autonomous-driving systems.

You will have significant ownership of your work while receiving regular guidance from experienced engineers and researchers. Together, you will define a realistic scope, discuss technical choices, and adapt the direction as new evidence emerges.

Depending on your chosen project, your work may include:

You are curious about how AI systems learn from and represent the physical world. You enjoy exploring challenging technical questions, testing ideas, and learning from results, including when they do not match your initial expectations.

You are currently pursuing a master’s degree in Computer Science, Machine Learning, AI, Robotics, Electrical Engineering, Engineering Physics, Applied Mathematics, or a related field.

We value different perspectives, experiences, and ways of approaching problems. You do not need to match every item below. We are interested in your technical foundation, learning ability, motivation, and potential to grow during the thesis.

Your thesis must be approved by a university examiner and supervisor and completed as part of a university-level course.

Helpful, but Not Required

Previous experience with the exact method named in your preferred project is not required. We encourage you to apply if you have relevant foundations and are motivated to learn.

Cluster B is for students interested in the technical foundations that make modern AI development possible.

You will explore technologies shaping the future of intelligent systems, from foundation models and generative AI to 3D reconstruction, sensor calibration, and large-scale data curation. At the same time, you will gain experience in turning an open research question into a structured investigation with meaningful results.

Your thesis will connect academic research with real technical challenges inspired by autonomous driving. You will have the opportunity to deepen your expertise, learn from experienced colleagues, and contribute your own perspective in an international engineering environment.

If you are excited by AI, computer vision, 3D understanding, generative models, or data-intensive machine learning, we would like to hear from you.

Read more about the projects on Cluster A and Cluster C, before you apply.

Practical Information

This role may have access to sensitive information, trade secrets, and confidential data. As part of the recruitment process, the selected candidate might undergo a background check.

→ META: master thesis, exjobb, examensarbete, degree project 2027, computer vision, deep learning, perception, self-supervised learning, world models, 3D reconstruction, Gaussian splatting, JEPA, autonomous driving, Gothenburg, Göteborg, Sweden, paid master thesis, Zenseact, Volvo Cars

This role may have access to sensitive information, trade secrets, and confidential data. As part of the recruitment process, the selected candidate might undergo a background check.

More about Zenseact

Our software makes a difference.

Using AI-based technology to create the ultimate driver support, we’re fighting to end car accidents and make roads safe for everyone. Every year, around 1,4 million people die in traffic while approximately 50 million people get injured. Many get disabled as a result of their injury. We can do better.

One purpose, one product.

We’re a software company dedicated to revolutionizing car safety. By designing the complete software stack for autonomous driving and advanced driver-assistance systems, we’re fighting to end car accidents and make roads safe for everyone. Zenseact was founded by Volvo Cars, and the teams are based in Gothenburg and Lund, Sweden and Munich in Germany. When we aim for zero accidents faster, we strive to speed up the transition to safe automation. This is essentially achieved by making cars updatable – like a computer or a phone. With regular software updates, a vehicle can be made safer long after its production. By accelerating improvement loops, shortening development cycles, and deploying high-capacity software quickly, we can make cars safer, and faster.

Culture with people at heart

To achieve our mission of saving lives and ending traffic accidents we must go where nobody has before. It requires us to venture into the unknown, pioneering new technology and pushing the frontier of autonomous driving. While there’s no denying our determination and expertise, we must stand united to succeed. By fostering a culture of support and enablement – a place of psychological safety where all of us can thrive – everything else will follow. We call this a people-at-heart culture. This culture means caring. It means the company cares about me, and we care about one another. It means sharing, so we give each other energy and have fun together. Our culture is also about belonging. It’s important to feel at home and that we can be ourselves at work. Finally, a people-at-heart culture means well-being. So, we enjoy the flexibility needed to be and do our best – at work and in life.

Zenseact works proactively to create a culture of diversity and inclusion, where individual differences are appreciated and respected. To drive innovation we see diversity as an asset, which means we value and respect differences in gender, race, ethnicity, religion or other belief, disability, sexual orientation or age, etc.

Interviews are held continuously, so we highly recommend that you submit your application at your earliest convenience.