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Volvo Group · Göteborg, SE

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

Göteborg, SE Data & Analytics 

Listed on Volvo Group’s official careers site. Listed as of November 1, 2026. Check that it is still open and apply there.
[Apply on Volvo Group’s careers site →](https://jobs.volvogroup.com/job/G%C3%B6teborg-Master-thesis-Understanding-Retarder-Stalk-Usage-Through-Data-Analytics-and-Machine-Learning-417-15/1374080055/)

[OpenNash Jobs](https://jobs.opennash.com/) / [Volvo Group](https://jobs.opennash.com/companies/volvo-group/) / Master thesis - Understanding Retarder Stalk Usage Through Data Analytics and Machine Learning

## Job description

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 We value your data privacy and therefore do not accept applications via mail.

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.

## At a glance
- Posted by the employer: Not stated
- First seen by OpenNash: Oct 25, 2026
- Last checked: Nov 1, 2026
- Listed for: 7 days
- Location: Göteborg, SE[Apply on Volvo Group’s careers site →](https://jobs.volvogroup.com/job/G%C3%B6teborg-Master-thesis-Understanding-Retarder-Stalk-Usage-Through-Data-Analytics-and-Machine-Learning-417-15/1374080055/)

## Hiring at Volvo Group
- Open jobs: 704
- New in the last 7 days: After two weekly collections
- Tracked since: Jul 31, 2026

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