ZENSEACT AB

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 (THIS PROJECT IS IN LUND, SWEDEN) 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 What you bring: * Programming experience, preferably in Python * An interest in machine learning, planning, optimization, simulation, control, or safety * Ability to learn from and evaluate scientific research * Ability to communicate technical ideas and findings in English HELPFUL, BUT NOT REQUIRED Experience in machine learning, reinforcement learning, planning, robotics, simulation, safety-critical systems, or research-driven software development will be valuable. This may come from coursework, research projects, internships, open-source contributions, or personal projects. 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. Read more about Cluster A and Cluster B, before applying. Practical Information * Thesis period: Spring 2027 * Location: Gothenburg, Sweden and Lund, Sweden for one specific project. * 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 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.

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/ YOUR MISSION 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: * Reviewing relevant scientific literature * Preparing and analyzing image, LiDAR, point-cloud, or annotation data * Designing and implementing experiments * Training or adapting models and 3D representations * Defining suitable baselines, metrics, and evaluation methods * Investigating performance, limitations, and failure cases * Documenting your work and presenting your conclusions WHO YOU ARE 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. WHAT YOU BRING * Programming experience, preferably in Python * Academic or practical experience in machine learning, deep learning, computer vision, data analysis, geometry, or a related area * Ability to read and evaluate scientific literature * Ability to communicate technical reasoning and results in English * Eligibility to complete the thesis as part of a university-level course Your thesis must be approved by a university examiner and supervisor and completed as part of a university-level course. HELPFUL, BUT NOT REQUIRED Depending on the project, experience in one or more of the following areas may be useful: * Deep learning frameworks such as PyTorch * Foundation models, self-supervised learning, or generative AI * Computer vision, LiDAR, point clouds, or 3D perception * Sensor calibration, neural rendering, or 3D Gaussian Splatting * Data pipelines, GPU computing, or large-scale experimentation * Research projects, publications, or open-source contributions 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. WHY CLUSTER B? 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 * Thesis period: Spring 2027 * Location: Gothenburg, Sweden (project B6 is in Lund) * 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 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.

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 students with strong technical foundations, curiosity, and a desire to learn. 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. Check out Cluster B and Cluster C aswell, before you apply. 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 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.