Master thesis - Understanding Retarder Stalk Usage Through Data Analytics and Machine Learning

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.

Modern heavy-duty trucks generate large amounts of vehicle data, offering valuable insight into driver behavior and vehicle performance. The retarder stalk lets drivers control auxiliary braking, but little is known about how drivers use its different positions, how often they switch between them, and how these patterns relate to braking behavior. By applying statistical and machine learning methods to real-world vehicle data, we can uncover usage patterns, identify what drives them, and support more efficient and intelligent vehicle control strategies.

We are looking for two Master’s students who want to apply data analytics and machine learning to real-world vehicle data, and to contribute to data-driven improvements in heavy-duty vehicle development

Thesis Scope

The purpose of this thesis is to investigate how drivers interact with retarder stalk positions under real-world driving conditions and how different usage patterns relate to braking behavior and vehicle operation.

The thesis is expected to:

• Analyze real-world vehicle data to characterize retarder stalk usage across driving sessions.
• Identify common usage patterns in stalk position distributions, transition sequences, dwell times, and switching frequency (e.g. Markov chain models).
• Discover driver usage profiles, such as stable usage, frequent changes, and short-term repeated transitions, using clustering methods (e.g. GMM, HDBSCAN) and latent-state models (e.g. HMM).
• Investigate the relationship between stalk position changes and braking behaviour before, during, and after transitions.
• Assess how driving conditions, such as vehicle speed, road gradient, and vehicle load, influence observed usage patterns.

This project offers a unique opportunity to work at the intersection of data science, vehicle systems, driver behavior, and automotive engineering while addressing a real industrial challenge using high-frequency vehicle data.

Who We Believe You Are

You are currently pursuing a Master’s degree in:

• Data Science
• Computer Science or Computer Engineering
• Electrical Engineering
• Applied Mathematics

You have a strong interest in:

• Data analytics and machine learning
• Time-series analysis, clustering, and behavioral pattern recognition
• Vehicle systems and automotive engineering
• Statistical analysis, visualization, and data-driven problem-solvin
• Experience with Python, large-scale datasets, or machine learning is considered an advantage. Familiarity with vehicle data or signal analysis is a plus.

What You'll Gain

• Hands-on experience applying data analytics and machine learning to real-world, high-frequency vehicle data
• Insight into how data science supports heavy-duty vehicle development in an industrial setting

Practical Information

Thesis Level: Master's Thesis
Number of Students: 2
Language: English 
Semester: Spring 2026

Application Deadline: November 6, 2026

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Who we are and what we believe in 
We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.

Applying to this job offers you the opportunity to join Volvo Group. Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. ​We are passionate about what we do, and we thrive on teamwork. ​We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment. 

Trucks Technology & Industrial Division hire team players who are ready to create real customer impact. Our decentralized teams work close to our customers, with speed and autonomy, to build what they truly need. 
Join us to collaborate on innovative, sustainable technologies that redefine how we design, build, and deliver value. Bring your curiosity, your expertise, and your collaborative energy, and together, we’ll turn bold ideas into tangible solutions for our customers and contribute to a more sustainable tomorrow.