Master Thesis - Using LLMs for FMEA-Based Anomaly Detection and Root Cause Analysis for FCEV
Göteborg, SE, 417 15
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.
What you will do
In this thesis project, you will use Large Language Models (LLMs) and machine learning to build a workflow that spans from structured reliability analysis to detection and diagnosis.
You will have the opportunity to:
- Use truck specifications and system descriptions to generate and structure FMEAs for selected fuel cell truck components and functions
- Define how the FMEA can be operationalized into signals, metrics, and hypotheses that are testable in logged vehicle data
- Design and prototype machine learning based anomaly detection methods to identify early signs of issues before they manifest as vehicle problems
- Develop approaches for root cause analysis that connect detected anomalies to likely underlying causes, supported by FMEA structure and data evidence
- Work with a large amount of logged data from a limited number of trucks, and document a reproducible workflow, assumptions, and results
Who are you?
We are looking for two highly motivated students who enjoy combining system understanding with data-driven methods and can navigate both engineering documentation and large-scale datasets.
Qualifications:
- Master Thesis student in Data Science, Machine Learning, Engineering Physics, Electrical/Mechanical Engineering, Computer Science, or similar
- Experience with Python and using LLM:s like ChatGPT
- Interest in reliability engineering, anomaly detection, diagnostics, or safety analysis
- Experience with LLMs (prompting, evaluation, RAG, agents, or similar) is a plus.
- Joint applications are encouraged. Prior experience working together (for example in previous projects) is considered meritorious and can support an efficient collaboration setup.
- For a joint application both candidates need to submit their CV. Please make it clear in your application/CV who you are applying with.
What’s in it for you?
This thesis offers a unique opportunity to work with real fuel cell truck data and contribute to methods that can improve uptime, quality, and development speed. You will explore how LLMs can support structured engineering processes like FMEA, and how machine learning can turn logged data into early warnings and actionable diagnostic insights.
Project start: August 2026.
Last application: June 4th
Contact person:
Björn Lindenberg, (Industrial Supervisor), Volvo Group Trucks Technology, Göteborg, Sweden
E-mail: bjorn.lindenberg@volvo.com
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.