Are you a student eager to apply your theoretical knowledge and fresh perspectives to real-world challenges? At Saab, we believe that innovation thrives on new ideas, and your master thesis project could be the spark that ignites our next technological breakthrough.
We recognize the immense value that students bring to our company. Your academic rigor, combined with your enthusiasm for cutting-edge technology, allows you to approach problems with a unique and insightful lens. At Saab, you’ll have the opportunity to collaborate with experienced engineers and specialists, gaining invaluable practical experience while making a tangible contribution to our growth and development.
Background
Isolation Forest and K-means are popular unsupervised machine learning algorithms used for anomaly detection, capable of identifying outliers or anomalies in datasets generated through logging or recording. Threshold levels are critical in determining whether an identified anomaly indicates an actual problem: a lower threshold produces a higher number of events flagged as anomalies, requiring more extensive manual post-analysis.
Description of the Thesis Project
This MSc thesis project aims to investigate how unsupervised ML algorithms can be used to identify problems within simulators of large-scale, complex systems, and how the results can best be structured and presented. The study will include a review of related previous work, interviews with senior software development engineers at Saab Aeronautics, and an evaluation using one or several ML models relevant to anomaly detection.
Team Rigs and Integration are responsible for the overall integration of our Development Simulators and offers a unique opportunity to gain insight in how a complex aircraft system works. We work in close cooperation with our stakeholders and with teamwork, innovation and curiosity we develop the simulators of today and tomorrow.