Master Thesis Development of New Data-Driven Method for Metal Fatigue Assessment

Master Thesis Development of New Data-Driven Method for Metal Fatigue Assessment

Full-time

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Job Description

In recent years, neural networks/machine learning methods have attracted increasing attention due to their ability to model complex, non-linear interactions between given input and output variables. This property is a great strength, but the training requires rather large data sets and the interpretability due to the black-box 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 data-driven approach for learning analytical equations. Implementations are currently limited and research in this field is ongoing.

• Contribute to the field of new explainable data-driven models and enhance their usage in engineering applications.
• Develop the new data-driven methodology and implement it in a Python-based toolbox. Investigate assumptions, limitations, and boundary conditions of the new method on artificial data.
• Test the methodology on material fatigue assessment data in a real-world scenario and compare it to a state-of-the-art fatigue assessment method for reliability.
• Ensure robust training by focusing on 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: Ability to communicate ideas clearly, organize tasks efficiently, and take responsibility; work effectively with others while maintaining 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 enrollment at a 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.