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

Zenseact; Lunds kommun; On-site; SEK 246,000 - 312,000; Full time

Job summary

Zenseact is seeking a Master’s thesis candidate to work on Cluster C projects in Sweden (Gothenburg and Lund). The role blends research with real-world experimentation on autonomous driving, leveraging datasets, simulation, and state-of-the-art AI methods to study how intelligent systems reason, plan, and act under uncertainty.

You will collaborate with engineers and researchers, explore reinforcement learning, world models, and safety-focused metrics, and contribute to actionable insights and

Qualifications

• Proficiency in Python and scientific computing.
• Interest in machine learning, planning, optimization, simulation, control, or safety.
• Ability to learn from and evaluate scientific research.
• Ability to communicate technical ideas in English.

Responsibilities

• Work with autonomous-driving datasets, simulation environments, and modern AI methods to investigate how intelligent systems reason, plan, and decide under uncertainty.
• Develop and evaluate algorithms for thesis projects and safety metrics.
• Design experiments and safety metrics to assess robustness of autonomous driving solutions.
• Investigate edge cases and failure scenarios and present findings.
• Present findings and recommendations with guidance from engineers and researchers.

Skills

Python

Machine learning

Reinforcement learning

Planning

Simulation

English communication

Education

Master's degree in CS/AI/Robotics

Tools

PyTorch

Job description

Choose Your Project

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.