Master Thesis, 30 HP: ML-based anomaly detection in simulators for a fighter aircraft
Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Science · Software Engineering
In short
Responsibilities
- Investigate unsupervised ML algorithms for anomaly detection in live data streams.
- Find relationships between early detected anomalies and later occurring problems in simulations.
- Conduct a review of related previous work.
- Conduct interviews with senior software development engineers at Saab Aeronautics.
- Evaluate one or several ML models relevant to anomaly detection.
Requirements
- Student at the end of Master of Science in Computer Science and Engineering, Industrial Engineering and Management, or Information Technology.
- Interest in software development and software testing.
- Must pass security vetting.
- Suitable start time for the work is in January 2026.
Desired Qualifications
- Theoretical knowledge and fresh perspectives.
- Academic rigor and enthusiasm for cutting-edge technology.
Benefits
- Opportunity to collaborate with experienced engineers and specialists.
- Gain invaluable practical experience.
- Make a tangible contribution to Saab's growth and development.
- Support and guidance to translate theoretical knowledge into practical solutions.
- Be a driving force behind Saab's technological advancements.
Skills
About Saab
As a leading defence and security company, we offer solutions that range from the depths of the oceans to high in the sky, on land and in cyberspace, to keep people and society safe. Empowered by our 22,000 talented people, we constantly push the boundaries of technology to create a safer, more sustainable and more equitable world. We design, manufacture and maintain advanced systems in aeronautics, weapons, command and control, sensors and underwater systems. We are headquartered in Sweden and have major operations all over the world and are part of the domestic defence capability of several…
Company
SaabJob Posted
3 days ago
Expires
in 1 week
Employment Type
Internship
Work mode
On Site
Experience Level
Student
Locations
Linköping, Sweden
Qualification
Applicants
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