Master Thesis Program 2027, Cluster C: Planning, Decision-Making and Safety

Help autonomous systems decide what to do next How should an autonomous vehicle act in a complex and uncertain world? At Zenseact, we're building systems that reason about risk, predict outcomes, plan safe behaviors, and make decisions in situations where safety matters. Cluster C focuses on the intelligence layer of autonomous driving, where AI moves beyond understanding the world to deciding how to act within it. As a thesis student, you'll work on research challenges spanning reinforcement learning, planning, verification, simulation, safety assessment, world models, and control systems. Your work will contribute to technologies designed to help autonomous systems make safer and more reliable decisions. Choose Your Project C1: Scenario Trajectory Parameterization Develop more efficient and meaningful representations of traffic trajectories and driving scenarios for analysis and planning. C2: Adaptive Stress Testing of Safety-Critical Automotive Software using Adversarial Reinforcement Learning Explore how reinforcement learning can discover challenging edge cases and safety-critical situations that traditional testing might miss. C3: Reasoning / QA Training of VLA Models for Parking Investigate reasoning capabilities in modern Vision-Language-Action models and their application to autonomous parking systems. C4: Object-Level Trajectory Planning Using World Models Research how world models can support better decision-making and trajectory planning in complex driving environments. C5: Virtual Verification First for E2E AD Software Development Framework Study how simulation and virtual verification can improve the development and validation of autonomous driving software. C6: Clustering Methods for Cut-In Scenarios Develop techniques to organize, categorize, and understand challenging traffic interactions involving cut-in behavior. C7: Evaluation of Predictors for Safety Assessment Investigate methods for assessing how predictive models contribute to safer autonomous driving decisions. C8: Neural Controller for Parking Scenarios Explore neural-network-based control systems for automated parking applications. C9: Evaluation of Search-Based Falsification Methods for Safety-Critical Driving Software Research methods for systematically uncovering weaknesses and failure modes in autonomous driving software. Read more about each project here: Cluster C Your Mission Every Cluster C thesis combines research with real-world experimentation. You will work with autonomous-driving datasets, simulation environments, and modern AI methods to investigate how intelligent systems reason, plan, and make decisions under uncertainty. Depending on your project, you may: Develop and evaluate algorithms Work with simulation and virtual testing environments Design experiments and safety metrics Investigate edge cases and failure scenarios Analyze planning, prediction, or controller behavior Evaluate robustness, performance, and safety Present findings and recommendations You'll have significant ownership of your work while receiving guidance from experienced engineers and researchers. Who You Are We're looking for curious students who enjoy solving difficult problems and exploring new ideas. You're currently pursuing a Master's degree in Computer Science, AI, Machine Learning, Robotics, Electrical Engineering, Engineering Physics, Applied Mathematics, or a related field. We value different perspectives, backgrounds, and ways of approaching problems. You don't need to meet every preferred qualification to apply. Strong foundations, curiosity, and a willingness to learn matter most. Essential Programming experience, preferably in Python Interest in machine learning, planning, optimization, simulation, control, or safety Ability to read and evaluate scientific literature Ability to communicate technical work in English Helpful, but Not Required Experience in one or more of the following areas: Reinforcement learning Deep learning and world models Planning and optimization Control theory and robotics Simulation environments Safety-critical systems PyTorch or similar frameworks Research projects, publications, or open-source contributions Why Cluster C? Cluster C explores some of the most challenging questions in modern AI: How should an intelligent system make decisions under uncertainty? How can we measure whether a decision is safe? How do we uncover failures before they occur in the real world? How can learning-based systems remain reliable in complex environments? You will work at the intersection of artificial intelligence, robotics, planning, simulation, and safety-critical software, using modern tools and realistic autonomous-driving scenarios. If you're excited by reinforcement learning, reasoning, planning, decision-making, simulation, or AI safety, Cluster C 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 eve