Thesis Work - Automation utilizing Computer Vision

Scania Group; Södertälje kommun; On-site; SEK 134,000 - 179,000; Part time

Job summary

Scania Group invites a Master’s student to work on a 20-week thesis in Smart Factory Lab, focusing on multi-camera computer vision for improved detection and verification in production/logistics. The project aims to integrate Scania’s standard CV tool across multiple viewpoints to enhance robustness in industrial use cases.

Start date is January 2026, with interviews ongoing. A background check may occur, and the work is conducted on-site at Scania’s Södertälje facility.

Qualifications

• Master's student in a relevant field; start Jan 2026.
• Experience with vision? beneficial but not required.
• Ability to work with cross-functional teams.

Responsibilities

• Explore multi-camera information fusion to improve detection and verification.
• Evaluate robustness and accuracy of object detection across views.
• Develop a proof-of-concept using Scania tools in an industrial setting.

Skills

Computer vision

Multi-camera reasoning

Data analysis

Industrial integration

Education

Master's program enrollment

Tools

Scania CV tool

Job description

30 hp - Smart Factory Lab

Introduction

Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project.

Background

Scania has recently standardized its approach to computer vision in production and logistics by providing a global software tool. This supports the company’s goals of increasing automation, building in-house knowledge, and advancing digital transformation.

Previous work has explored how this technology can be applied within logistics, including the use of computer vision for detecting and reading information on incoming material and boxes. Building on this work, there is a need to further investigate how computer vision can be used in more complex scenarios where objects need to be detected and verified from multiple viewpoints.

In many industrial applications, a single camera may not provide enough information to reliably detect or verify all relevant features of a three-dimensional object. Occlusion, viewing angle, object orientation, and environmental conditions can all affect the robustness of the solution.

Objective

The work will investigate how information from multiple camera views can be combined to improve the reliability of object detection and verification compared with using a single camera. This may include reasoning about the spatial relationship between cameras, objects, and detected features.

The solution will be developed using Scania’s standardized computer vision tool, ensuring that the work contributes to the continued development of scalable and reusable computer vision solutions within Scania.

The goal is to explore best practices for multi-camera computer vision, develop a working proof of concept in a relevant industrial use case, and evaluate how multi-camera spatial reasoning can improve robustness, detection accuracy, and verification of objects and their features.

Key questions (Inspiration)

• How can information from multiple cameras be combined to improve detection and verification of data and spatial objects relationships?
• How can Scania’s standardized computer vision tool be applied, adapted, and integrated to support multi-camera solutions and ensure scalability across different production and logistics applications?
• What are the advantages and limitations of a multi-camera approach compared with a single-camera solution?

Education/program/focus

Master's program in machine engineering, production engineering, management, computer science, data science, or related fields.

Number of students: 1

Start date: January 2026

Estimated time needed: 20 weeks

Contact persons and supervisors

Robin Törnblom, [e-mail]

Application

Your application must include a CV, personal letter and transcript of grades

A background check might be conducted for this position. We are conducting

interviews continuously and may close the recruitment earlier than the date

specified.

Publication date from-to.

2026-10-01–2026-11-22