Om tjänsten
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:
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:
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:
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
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.