Internship: Physics-informed machine learning for variable amplitude loading in fatigue crack growth
NLR - Netherlands Aerospace Centre
Internship: Physics-informed machine learning for variable amplitude loading in fatigue crack growth
Master thesis internship at NLR on physics-informed machine learning for variable amplitude fatigue crack growth in aircraft structures. The project involves developing a PIML framework, working with real-world datasets, and validating against state-of-the-art methods. Requires MSc in Aerospace/Mechanical Engineering, Physics, Computer Science or related field, with Python and machine learning experience.
Internship: Physics-informed machine learning for variable amplitude loading in fatigue crack growth
Master thesis internship at NLR on physics-informed machine learning for variable amplitude fatigue crack growth in aircraft structures. The project involves developing a PIML framework, working with real-world datasets, and validating against state-of-the-art methods. Requires MSc in Aerospace/Mechanical Engineering, Physics, Computer Science or related field, with Python and machine learning experience.
Salary
Core Qualifications
Technical (Must-have)
Soft Skills
Key Responsibilities
- Preliminary assessment of available PIML for variable amplitude fatigue crack modelling
- Literature review to analyze the application of PIML within PHM to support aircraft health management
- Designing and implementing a PIML framework to model VA fatigue crack growth
- Validating the framework using multiple real-world datasets and benchmarking against state-of-the-art methods