Master Thesis: Enabling Machine Learning in Embedded Engine Control Development

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.

Description Of Thesis Work At Volvo Group, we develop embedded control solutions for engine and powertrain applications using model-based development methods and tools such as Simulink and TargetLink.

This thesis is conducted in a cross-functional setting, combining expertise in engine control development with software infrastructure integration. It offers an opportunity to work at the intersection of control engineering, embedded software, and machine learning for automotive applications.

Machine learning methods are becoming increasingly relevant for automotive control systems, offering opportunities to complement traditional physics-based approaches with data-driven models. In embedded software development, however, adoption is limited by requirements on computational performance, memory usage, maintainability, and compatibility with existing workflows.

The objective of this thesis is to investigate how machine learning methods can be integrated into automotive embedded software development. The work will include development, integration, and evaluation of a machine learning model in an embedded engine control application, with focus on tooling, deployment, and fit with existing model-based development processes.

Last date of application - 30th October 2026 Scope: Master thesis, 30 ECTS, approximately 6 months.

Profile: Students in Control Engineering, Embedded Systems, Computer Science, Machine Learning, Electrical Engineering, or similar.

Location: Göteborg

Number of students: 2

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