Master Thesis, 30 HP: Visualization of big data from ML-based anomaly detection in simulators
Technology, Data & Digital · Data, AI & Analytics · Data Science · Software Engineering · Data Visualization
In short
This Master Thesis project at Saab focuses on visualizing big data from ML-based anomaly detection in simulators. Students will apply unsupervised ML algorithms like Isolation Forest and K-means to identify problems in complex systems and present the results effectively, collaborating with experienced engineers.
Responsibilities
- Investigate how unsupervised ML algorithms can be used to identify problems within simulators of large-scale, complex systems.
- Structure and present the results of ML-based anomaly detection.
- Conduct a review of related previous work.
- Conduct interviews with senior software development engineers at Saab Aeronautics.
- Evaluate using 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.
- Ability to pass a security vetting.
- Interest in machine learning algorithms (Isolation Forest, K-means).
- Interest in data visualization.
Desired Qualifications
- Experience with ML models relevant to anomaly detection.
- Familiarity with big data visualization techniques.
Benefits
- Opportunity to apply theoretical knowledge and fresh perspectives to real-world challenges.
- 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.
#thesis#master thesis#big data#machine learning#anomaly detection#simulators#visualization#software development#software testing
Company
Saab
Job Posted
1 week ago
Expires
in 1 month
Employment Type
Internship
WorkMode
On Site
Experience Level
Entry
Locations
Linköping, Sweden
Qualification
Master
Applicants
Be an early applicant