Objective
Investigate how AI can improve data quality assurance by automatically identifying anomalies and rare phenomena in vehicle sensor data.
Keywords: AI, machine learning, data quality, anomaly detection, automotive software, quality assurance
Summary
Reliable data is the foundation of high-performing automotive software. At NIRA Dynamics, incoming vehicle sensor data is analyzed to ensure its quality before it is used in product development and validation. While existing processes catch many issues, parts of the quality review still depend on manual inspection by experienced engineers. This can be time-consuming and makes it difficult to detect subtle or rare phenomena.
This thesis aims to explore how artificial intelligence can support and extend this process. The focus is on using machine learning techniques to identify anomalies, outliers, and unusual patterns in large-scale datasets. By comparing classical statistical methods with modern AI approaches, the project will assess how effectively AI can increase efficiency, reduce reliance on manual work, and reveal quality issues earlier in the workflow.
Your profile
We are looking for an engineering student who is studying a master's program with a specialization in Y, D, M, E, F, Z, or equivalent. Knowledge within any of the following areas is beneficial:
We expect you to have a solid academic record, be driven, take initiative, and work independently. You will be carrying out the thesis at our head office in Linköping.
We are looking forward to your application! Do not forget to include a personal letter, CV, and course listing with grades. We will be considering applications on a rolling basis. The earliest expected start date is January 2026.
About NIRA
At NIRA Dynamics, we believe in making roads safer by delivering intelligent software solutions directly into passenger cars. Within our Wheel Safety Insights (WSI) department, we develop advanced in-vehicle algorithms that enhance safety, reliability, and performance — helping cars better understand their wheels, tires, and the road beneath them. Just like our vision of next level of mobility includes all people and all modes of travel, our workplace thrives on diversity and inclusion. The broader the mix of experiences and perspectives we have, the stronger we are in shaping a safer and more sustainable transportation system for everyone.
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