Master Thesis, 30 HP: ML-based Anomaly Detection in Simulators for a Fighter Aircraft

This Master Thesis project investigates the use of unsupervised machine learning algorithms, such as Isolation Forest and K-means, to identify anomalies in live data streams from fighter aircraft simulators. The role involves reviewing previous work, interviewing senior engineers, and evaluating ML models to find relationships between early simulation anomalies and later problems. Students will gain practical experience in software development and testing while contributing to Saab's technological advancements.