Thesis Work: Vision-based Quality Inspection

Are you passionate about AI, computer vision, and smart manufacturing? Do you want your thesis work to contribute directly to improving quality in a high-tech production environment? Apply here 🎯 At Westermo, quality is at the heart of everything we do. As we continue to invest in digitalisation, automation, and AI, we see great opportunities to further strengthen how quality is secured in our manufacturing processes.

For the spring semester of 2027, we are offering an exciting Master's or Bachelor's thesis opportunity where you will develop and evaluate a vision-based operator support system for quality inspection in electronics production. About the Quality Team The Quality Department in Stora Sundby plays a central role in ensuring that Westermo’s products and manufacturing processes maintain the highest standards.

The team works closely with Production, Engineering, Supply Chain, and customers to drive continuous improvements, analyse quality trends, handle non-conformances, and strengthen quality performance throughout the entire product lifecycle. Quality Engineers are involved from new product introduction through production, warranty, and after-sales activities, making quality an integrated part of everything we do.

As part of your thesis, you will collaborate with experienced quality and manufacturing professionals and gain valuable insight into how modern quality assurance is carried out in a global technology company. Are You Our Next Thesis Student in Vision-Based Quality Inspection? In our factory in Stora Sundby, approximately 120 employees manufacture high-quality industrial networking products that are delivered to customers and partners around the world.

We are continuously investing in new technologies, equipment, production flows, and digital capabilities to support our future growth. This thesis project focuses on developing a vision-based operator support solution capable of: Identifying products automatically through image recognition Visualising predefined control areas (ROIs) Verifying critical mounting points through image analysis Supporting operators in performing quality inspections more efficiently and consistently Evaluating the feasibility and benefits of using AI-driven vision systems within electronics manufacturing The outcome of the project will contribute to our ongoing digitalisation journey and help explore new ways of enhancing quality assurance in production.

Requirements

Currently studying at a Swedish university Knowledge of image analysis and programming Experience with Python or similar programming languages Swedish personal identity number (personnummer) or coordination number (samordningsnummer) Meritorious: Experience with OpenCV Experience with YOLO Experience with Tensor. Flow Knowledge of machine learning and deep learning Experience from manufacturing or industrial environments In Return, We Offer: An opportunity to work on a real industrial challenge with direct business relevance Access to a modern manufacturing environment and advanced production technologies Guidance from experienced engineers within Quality and Operations A collaborative and supportive culture where knowledge sharing is encouraged Insight into how AI and digitalisation are transforming modern manufacturing The possibility to build valuable industry experience and professional networks Basic Information: Location: Wij 4, Stora Sundby, Sweden Thesis Level: Bachelor’s or Master’s Thesis Number of Students: 1-2 students Start Date: Spring Semester 2027 In your application, please include the following: CV/Resume Transcript of records Application letter describing your interest in the thesis topic If applying together with another student, both students should submit separate applications and reference each other in the application.

Last day to apply: 1 November 2026 Please note that selection is ongoing and candidates may be chosen before the application deadline. For further information, please contact: Elin Sandell, Recruiter elin.sandell@westermo.com