Master Thesis -Machine Learning for Proactive Maintenance of Construction Machines

Job description

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:

Predicting potential failures in machinery can enable proactive maintenance work. Proactive refers to the ability to plan and coordinate maintenance work well in advance. There is a real need to ensure that machines don't fail during operations as any machine downtime is a significant revenue loss for users of construction machines.

The focus of the thesis is driveline systems. These systems include engines (either ICE or Electric), Transmission, Axles as well as hubs and brakes.

 

Description:

Construction equipment such as wheel loaders, haulers, excavators etc., are used for demanding work. Breakdowns are expensive and result in loss of productivity. The thesis aims to predict failures to take preventive action. Predictive Maintenance is also referred to as Proactive Maintenance. Volvo Construction Equipment (VCE), which is part of Volvo Group, has been practicing proactive maintenance in the form of Active Care.

The task is to analyze data using machine learning approaches to further improve our ability to predict future maintenance work. This is an important strategic area as it will support sustainability goals of the company. The selected students can continue the work from previous years by extending and validating machine learning techniques that could be operationalized within our business processes.

 

Students applying for this work should be able to show their technical competence around machine learning or other relevant fields. These details should be described in your cover letter along with other relevant coursework. The work will involve collaboration with engineers at VCE Eskilstuna and therefore the students are expected to work out of Eskilstuna.

The thesis is for 30ECTS per student.

·  If you are a single applicant, you might need to collaborate with an additional thesis student.
·  If you are pair of students that want to work together, explain in your cover lever. Send them in two separate applications.

 

Suitable Background: Courses in Machine Learning and Data Science. Interest in Machinery.

Thesis Level: Master  

Language: English  

Starting Date: Early 2026 (Flexible)

Number of Students: 1 or 2  

 

L ast application date: 5 December 2025 and applications will be evaluated from the time of publications of this announcement.

Main Contact: Varun Gopinath (PhD) varun.gopinath@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. 

 

Part of Volvo Group, Volvo Construction Equipment is a global company driven by our purpose to build the world we want to live in. Together we develop and deliver solutions for a cleaner, smarter, and more connected world. By unleashing everyone’s full potential, we build a more sustainable future for all our stakeholders. Come join our team and help us build a better tomorrow. 

Job Category:  Technology Engineering

Organization:  Volvo Construction Equipment

Travel Required:  No Travel Required

Requisition ID:  26198