Master Thesis: Machine Learning on Field Test Data

"Founded in 1927, the Volvo Group is committed to driving prosperity and shaping the future landscape through sustainable transport, mobility, and infrastructure solutions. By offering trucks, buses, construction equipment, power solutions for marine and industrial applications, financing and services that increase our customers’ uptime and productivity. Our headquarters are in Gothenburg, Sweden. Volvo Group shares are listed on Nasdaq Stockholm." 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.  Thesis Background Volvo Penta has a vested interest in utilizing and understanding vast amounts of data. As Martin Lundstedt (Volvo Group President and CEO) recounts, “It’s important to have good data management and ownership [of data], because then on top, you can create a lot of smart solutions.” To that end, we at Volvo Penta Data Science & Engineering (VPDS&E) have applied data engineering principles to build a large-scale data lake. This has enabled us to create a data-driven and cloud-native workflow for advanced analytics using Microsoft Azure and Databricks. Along with our data-driven workflow, data engineering and analytics have enabled us to reach valuable strategic conclusions and optimizations. What you will do? Our purpose with this thesis is to study real world engine usage data to further our understanding of engine behavior and applications. Thanks to the wealth of data collected by the field test department, we see multiple different projects. We will present the outline for a project, but welcome applicants to come with their own ideas and suggestions. The thesis will focus on analyzing engine driving behavior by utilizing time series analysis techniques, including pattern recognition, on time-resolved engine signals. The main objective is to uncover the various driving patterns and behaviors demonstrated by our engine population. Key questions to investigate include; * What are the most common and unique driving patterns observed across one or more engines? * What clusters can be identified within the extracted patterns? From Volvo Penta’s perspective, this research represents a significant opportunity to enhance our products by developing more targeted tools that correlate specific driving patterns with various types of errors.  Qualifications for the Master Thesis We’re looking for curious, driven, and forward-thinking master’s students with a genuine interest in data science. Ideal candidates are enthusiastic about solving real-world problems, eager to explore new technologies, and capable of inspiring others through their innovative mindset. To qualify for this thesis role you should also:  * Be a master student in any of the following fields or related disciplines Artificial Intelligence * Computer Science * Data Science * Machine Learning * Physics * Have knowledge of machine learning algorithms and big data * Have Some programming proficiency Ready for your next move? The journey begins! Once you have submitted your Resume, Personal letter and Transcript of records, an email confirmation will be sent to you. The hiring team will review your application together with the hiring manager and shortlisted candidates will be contacted with information about the following steps. Are you excited to bring your skills and disruptive ideas to the table? We can’t wait to hear from you. Apply today! We review applications continuously, but the last application date is June 8th Do you have any questions about the role? Feel free to reach out to our manager Andreas!  Andreas Nyman, Manager Data Science & Engineering, andreas.nyman@volvo.com We value your data privacy and therefore do not accept applications via mail.