Internship: Physics-informed machine learning for variable amplitude loading in fatigue crack growth
Marknesse, Flevoland, Nederland
EUR 30K-32K (estimate) Entry-level Internship
Tasks
- Assess physics informed machine learning methods for variable amplitude fatigue crack modelling
- Benchmark performance against state of the art methods
- Conduct literature review on physics informed machine learning for prognostics and health management
- Design and implement physics informed machine learning framework for variable amplitude fatigue crack growth
- Quantify prediction uncertainty
- Validate models using real world datasets
Perks/Benefits
- Internship allowance
- Optional conference publication
- R and D project experience
- Technical staff support
- Thesis report contribution
- Weekly supervision
Skills/Tech-stack
Crack Growth | Data benchmarking | Fatigue crack growth | Health management | Machine Learning | Physics-informed machine learning | Prognostics and Health Management | PyTorch | Python | Uncertainty Quantification
Education
Roles
Engineer | Intern | Learning Engineer | Machine Learning Engineer
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