Master Thesis Motion Model 2026: Improve dynamics of autonomous vehicles with 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. 

Background

Automated driving is here to improve the productivity and safety of the confined area transport system. To get there a lot of verification needs to be carried out to make sure the application is safe and works as expected with improved productivity.

A key part in the testing of automated applications is simulation. Simulations are not only cheaper, but more efficient and far more scalable compared to testing on vehicles or in rigs. To get reliable simulations the models need to resemble the mission cycles with fidelity, a digital twin (DT) of the real world vehicle.

When it comes to heavy duty vehicles and machines, one very complex part is to model bulk such as rocks or sand which we now want to look deeper into.

Simulating bulk directly may be very computational heavy why a reduced order model based on machine learning would be beneficial to use, since it enables less computational effort for the purposefully chosen fidelity of the simulated model.

Keywords

Machine learning (ML), Digital Twin (DT), Reduced order model (ROM), Discrete element method (DEM), Automated transport

Objective

• Using discrete element method to create a high-fidelity model of bulk material.
• Integrating machine learning to develop a reduced order model of the material within our partner’s software. Use the model to simulate dynamic loading/unloading of material in a mission cycle.

Scope and Method

• Literature survey on existing methods and tools
• Get familiar with our and our partner’s tools.
• Implement a reduced order model of grain/bulk material that is portable to our tools
• Run and analyze loading and unloading simulations
• Write final report.

Who you are

To excel in this thesis a strong interest in modelling and simulation is beneficial. Suitable background is applied physics, mechatronics, vehicle engineering or similar.

The duration of the work will be 20 weeks / 30 ETCs on master level.

The location for this thesis will be carried out at the Volvo Autonomous Solutions office either in Gothenburg or Eskilstuna.

Why join us

At Volvo Autonomous Solutions, you’ll be part of a team that’s shaping the future of transport through cutting-edge technology and innovation. This thesis offers a unique opportunity to work hands-on with autonomous vehicle systems in a real-world industrial setting, supported by experienced engineers and access to state-of-the-art tools. You’ll gain valuable insights into functional safety standards and embedded systems while contributing to solutions that make mobility safer and smarter. Join us and take your first step toward a career that drives change.

We look forward to receiving your application letter, academic transcript, and CV (in English).

Contact information Volvo: Markus Örn, markus.orn@volvo.com

Last application date: 30th of November

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. 

Part of Volvo Group, Volvo Autonomous Solutions accelerates the development, commercialization and sales of autonomous transport solutions, focusing on defined segments for the on- and off-road space. The combination of strong tech expertise and skilled customer solutions creates innovative transport offers never seen before. We are constantly pushing our own skills and ability to drive change in a traditional industry to meet a growing customer demand. We are now looking for innovative, committed individuals to join us in our endeavor to create customer solutions that enhance safety, flexibility and productivity.