Master Thesis Projects 2027: Computer Vision & Perception for Autonomous Driving

Understanding the world is one of the fundamental challenges in artificial intelligence. At Zenseact, we build AI systems that must understand, interpret, and predict complex real-world traffic environments. Our perception and world-modeling technologies are trained on large-scale driving data and contribute to future autonomous-driving capabilities. Cluster A focuses on some of the most exciting areas in modern machine learning, including self-supervised learning, multimodal perception, world models, synthetic data, occupancy prediction, and 3D scene understanding. As a thesis student, you will work on open-ended research challenges where the answers are not yet known, contributing ideas that may influence future generations of perception systems. Choose your project: A1: Self-Supervised Learning for Camera-Radar Perception in Autonomous Driving Explore how self-supervised learning can improve multimodal perception by combining camera and radar data without relying heavily on manual labeling. A2: Self-Supervised Learning via a Joint Embedding Predictive Architecture Investigate predictive representation learning methods that allow models to learn directly from autonomous-driving imagery and large-scale unlabeled data. A3: Enhance Dynamic Object State Prediction Using Synthetic Data Study how synthetic data can improve prediction capabilities for dynamic agents and support more robust autonomous driving systems. A4: Towards Better Occupancy World Models for Autonomous Driving Develop new methods for understanding and predicting occupancy-based representations of the driving environment. A5: 3D-Grounded World Models for Autonomous Driving Explore how world models can incorporate 3D understanding and physical structure to better represent complex traffic environments. A6: Semantic Superquadric Splatting for Structured 3D Scenes Research novel approaches for semantic scene understanding using modern 3D representations and structured scene modeling. Read more about the projects here: https://zenseact.com/mtp_cluster-a/ Your mission and day-to-day tasks You will combine machine-learning research with hands-on experimentation using real sensor data and modern compute resources. Together with experienced researchers and engineers, you will: Review state-of-the-art research Work with large-scale sensor datasets Design and run deep-learning experiments Train and evaluate neural networks Build perception and world-model architectures Analyze model behavior and failure cases Present findings and recommendations You will have significant ownership of your thesis while receiving guidance from experts in the field. Qualifications & Experience We're looking for curious students who enjoy solving challenging problems and exploring new ideas. You are currently pursuing a Master's degree in Computer Science, AI, Machine Learning, Robotics, Engineering Physics, Electrical Engineering, Applied Mathematics, or a related field. Essential Strong Python programming skills Understanding of machine learning fundamentals Experience working with data and experimentation Ability to read scientific literature Strong English communication skills Helpful, but not required Experience in one or more of the following areas: Deep learning frameworks such as PyTorch or TensorFlow Foundation models, self-supervised learning, or representation learning Computer vision, sensor fusion, or world models Occupancy prediction or 3D scene understanding Research projects, publications, or open-source contributions Previous experience with the exact methods used in a project is not required. Strong fundamentals, curiosity, and a willingness to learn matter most. Why Cluster A? Cluster A combines cutting-edge machine-learning research with real-world deployment challenges. You will work on problems at the intersection of computer vision, representation learning, robotics, and autonomous driving, using large-scale driving datasets and modern AI infrastructure. If you are excited by understanding how intelligent systems learn about the world, and want to contribute to technology that could improve road safety at scale, Cluster A is built for you. Practical Information Thesis period: Spring 2027 Location: Gothenburg, Sweden Format: Individual applicants and thesis pairs are welcome Working model: Primarily on-site collaboration with flexibility when appropriate Eligibility: You must be enrolled in a Master's program and conduct the thesis as part of your university studies Legal requirement: You must have the legal right to study in Sweden during the full thesis period Application: Please rank your top three project choices when applying 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 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 e