Master Thesis - Data-Driven Test Strategy Optimization for Fault Detection in Automotive Electronic

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What we offer:

Group Summary:

Testing automotive Electronic Control Units (ECUs) is a complex engineering challenge. Modern electronic systems operate under countless combinations of voltages, temperatures, power cycles, restart sequences, and system loads.

Since it is impossible to test every possible operating condition, engineers must identify the most effective test scenarios to uncover hidden weaknesses and potential failures.

In this thesis project, you will investigate how available design and reliability data can be used to recommend test strategies that maximize the likelihood of detecting faults in ECU designs.

The project will focus on establishing relationships between:

• ECU design characteristics
• Component technologies
• Circuit functions
• Historical failure mechanisms
• Test conditions and operating scenarios

Using available engineering data, you will evaluate whether a method can be developed to automatically recommend optimized test cases and operating conditions for fault detection.

Throughout the project, you will collaborate with subject matter experts and gain insight into real-world automotive verification and reliability challenges.

Expetected Outcome
The objective of this thesis is to develop and evaluate a methodology for recommending test strategies and test conditions that increase the probability of revealing faults and weaknesses in automotive ECU designs.

Expected deliverables include:

• A proposed methodology for test strategy optimization
• Identification of key design parameters linked to fault occurrence
• Recommended operating conditions and test scenarios for fault detection
• Validation of the methodology using real ECU designs and historical failure cases
• Analysis of the effectiveness, strengths, and limitations of the approach
• Recommendations for future implementation within Magna's verification processes

The results have the potential to improve both verification efficiency and product reliability while reducing the risk of undetected design weaknesses.

Qualifications

You are a curious and motivated student with a strong analytical mindset and an interest in solving technical challenges. You enjoy learning new technologies, taking initiative, and collaborating with others to find innovative solutions. Most importantly, you are eager to apply your academic knowledge to a real-world engineering challenge within the automotive industry.

We are looking for students who:

• Are pursuing a Master's degree in Electrical Engineering, Computer Engineering, Engineering Physics, Mechatronics, Reliability Engineering, Data Science, or a related field
• Have a strong interest in automotive electronics and system reliability
• Possess analytical thinking and problem-solving skills
• Enjoy working with data, engineering models, and technical investigations
• Are able to work independently in a structured manner
• Communicate effectively in English
  

Application and Contact Information
If you have any questions regarding the thesis project, please contact Talent Acquisition Partner Per Lind, [email protected] or Manager, Morgan Mathiesen, [email protected]

Last application date: 2026-10-15. Please note that we review applications on an ongoing basis and the position may be filled before last application date.

Start: Spring 2027, 30 p Master
Number of Students: 1

Learn More About Magna

Want to learn more about Magna, our operations in Sweden, and how we work with recruitment, onboarding and employee development?

Visit our career page:
https://www.magna.com/careers/sverige

Awareness, Unity, Empowerment:

At Magna, we believe that a diverse workforce is critical to our success. That’s why we are proud to be an equal opportunity employer. We hire on the basis of experience and qualifications, and in consideration of job requirements, regardless of, in particular, color, ancestry, religion, gender, origin, sexual orientation, age, citizenship, marital status, disability or gender identity. Magna takes the privacy of your personal information seriously. We discourage you from sending applications via email or traditional mail to comply with GDPR requirements and your local Data Privacy Law.

AI-Assisted Screening Disclosure

As part of our commitment to a fair, consistent, and efficient recruitment process, we may use artificial intelligence (AI) tools to assist in the initial screening of applications submitted through our Workday system. These tools help identify qualifications and experience that align with the role requirements. Please note that AI is used solely to support our recruiters. Final decisions are always made by the hiring manager and the hiring team. Importantly, no applicant data is shared externally through these AI tools. All information remains securely within our systems and is handled in accordance with our privacy and data protection policies.

Under conditions defined by applicable law, you may have the right to request an explanation of how AI is used to support decision-making.

If you have any questions or concerns about this process, feel free to contact our Talent Attraction team.