Master thesis AI-Based Anomaly Detection for Production Verification
Help Shape the Future of Smart Manufacturing. Modern production systems generate vast amounts of verification data every day. Hidden within this data are patterns that can reveal quality deviations, process changes, and emerging issues long before they become visible through traditional limit-based evaluations. As manufacturing continues to embrace digitalization and artificial intelligence, there is a growing opportunity to use advanced analytics to improve quality assurance and decision-making.
We are looking for a Master's student who wants to explore how AI and anomaly-detection techniques can be applied to production verification and contribute to the development of smarter, data-driven manufacturing processes.
Thesis Scope
The purpose of this thesis is to investigate how artificial intelligence and anomaly-detection methods can be used to identify unusual patterns in verification data and strengthen decision support in a production environment.
The Thesis Is Expected To
Analyze production verification data from a relevant industrial use case. Identify characteristic patterns of normal system behavior. Evaluate and compare different anomaly-detection approaches. Develop methods for visualizing and interpreting detected deviations. Assess detection performance, false rejections, traceability, and practical applicability in a production environment.Deliver a proof of concept or methodology, together with recommendations for future implementation and development.
This project offers a unique opportunity to work at the intersection of artificial intelligence, data analytics, quality assurance, and manufacturing while addressing a real industrial challenge.
Who We Believe You Are
You Are Currently Pursuing a Master's Degree In
Data ScienceComputer EngineeringIndustrial EngineeringApplied MathematicsOr another relevant engineering discipline
You Have a Strong Interest In
Data analytics and machine learningArtificial intelligence and anomaly detectionManufacturing systems and industrial applicationsProblem-solving and analytical thinking
Experience within manufacturing or production environments is considered an advantage.
What You'll Gain
Hands-on experience applying AI and advanced analytics to real manufacturing challengesExposure to production verification, quality assurance, and digital manufacturingOpportunities to collaborate with experts in manufacturing, data analytics, and engineeringExperience developing solutions with direct industrial relevance and business impactValuable insights into how AI can support the future of smart factories
Practical Information
Thesis Level: Master's Thesis
Number of Students: 1
Location: Volvo Group
Language: English (mandatory), Swedish is a plus
Semester: Spring 2026
Application Deadline: November 30, 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.