JOB DESCRIPTION:

In order to support the Supportability Engineering team, Airbus Defence and Space is looking for a

MASTER STUDENT IN THE FIELD OF SUPPORTABILITY ENGINEERING (D/F/M), TITLE OF THESIS “MACHINE LEARNING BASED PATTERN RECOGNITION”

You are looking for a master thesis? Then apply now! We look forward to you supporting us in the Supportability Engineering team as a master student (d/f/m)!

• Location: Manching
• Start: 01.11.2025
• Duration: 6 months

DESIRED SKILLS AND QUALIFICATIONS

• Full time enrolled student (d/f/m) in Computer Science, Electrical Engineering, Data Science, Mathematics, or a related field
• Solid understanding of machine learning concepts and data preprocessing
• Practical experience with MATLAB Simulink and ML frameworks (e.g. scikit-learn, TensorFlow, PyTorch)
• Programming skills (e.g. Python)
• Interest in fault detection, diagnostics, and system testability
• Analytical mindset, structured working style, and ability to work independently
• Basic knowledge of test or diagnostic systems (e.g. FMEA/FMECA) is a plus
• Language skills: English fluent; French basic knowledge
  Please upload the following documents: cover letter, CV, relevant transcripts, enrollment certificate.
  Not a 100% match? No worries! Airbus supports your personal growth.
  Take your career to a new level and apply online now!
  This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.

EXPERIENCE LEVEL:

Student

• Analyse and preprocess diagnostic data from test systems
• Identify characteristic failure patterns and symptom relationships
• Define and compare suitable model architectures for fault detection and isolation (e.g. decision trees, ensemble models, neural networks, clustering approaches, etc.)
• Train and validate models using established ML frameworks (e.g. scikit-learn, TensorFlow, PyTorch) and MATLAB Simulink (e.g. Artificial Intelligence (AI))
• Evaluate model performance using metrics such as detection rate, false alarm rate, and isolation accuracy
• Integrate your approach into an existing analysis pipeline or tool environment
• Document and present your results to stakeholders