Master Thesis Development of New Data-Driven Method for Metal Fatigue Assessment
Master Thesis Development of New Data-Driven Method for Metal Fatigue Assessment
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Job Description
In recent years neural networks / machine learning methods have attracted increasing attention due to their ability to model complex nonlinear interactions between given input and output variables. On the one hand this property is a great strength. On the other hand the training requires rather large data sets and the interpretability due to the blackbox character is an obstacle for many engineering problems where small data sets and physical relationships or boundary conditions play an essential role. In engineering applications interpretable analytical equations are preferred not only for trustworthiness but also for understanding interpolations and extrapolations given the limited and heterogeneous data. Symbolic regression method is a datadriven approach for learning analytical equations. Implementations are currently limited and research in this field is ongoing.
- Therefore you will contribute to the field of new explainable datadriven models and enhance their usage in engineering applications.
- You will develop the new datadriven methodology and implement it in a Pythonbased toolbox. Assumptions limitations and boundary conditions of the new method should be investigated on artificial data.
- For a realworld scenario the methodology is tested on material fatigue assessment data and you should compare it to a stateofthe art fatigue assessment method for reliability.
- Big challenge: Robust training by ensuring stable numerical optimizations.
Qualifications :
- Education: Master studies in the field of Mathematics Computer Science Engineering or comparable
- Experience and Knowledge: strong mathematical background; strong programming skills (preferably in Python); knowledge of numerical optimization is a plus
- Personality and Working Practice: you are able to communicate your ideas clearly organize your tasks efficiently and take responsibility for them; you work effectively with others and maintain focus on team objectives
- Languages: very good in German or English
Additional Information :
Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.
Need further information about the job
Steve WolffVorbeck (Functional Department)
49 7
Christian Frie (Functional Department)
49 1
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
