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

As a Master Thesis candidate for the new data-driven method for metal fatigue assessment at Bosch, your responsibilities will include developing a new data-driven methodology, implementing it in a Python-based toolbox, and conducting research on its assumptions, limitations, and boundary conditions using artificial data. You will also test the methodology on real-world material fatigue assessment data and compare it against existing techniques to ensure reliability and effectiveness.

To be considered for the Master Thesis position at Bosch, candidates are expected to be enrolled in a Master's program in Mathematics, Computer Science, Engineering, or a related field. A strong mathematical foundation, proficient programming skills—especially in Python—and knowledge of numerical optimization are highly advantageous. Furthermore, candidates should be effective communicators and able to collaborate within a team.

The Master Thesis at Bosch is designed to last for a duration of six months. The start date can be arranged according to your schedule and in agreement with the functional department involved in your research, providing some flexibility to accommodate your academic commitments.

Yes, for the Master Thesis position at Bosch, candidates should possess very good communication skills in either German or English. This ensures that you can effectively collaborate and share ideas with the team while engaging in discussions regarding your findings and methodologies.

Bosch places a strong emphasis on Diversity and Inclusion, which are integral to our corporate culture. Throughout the Master Thesis hiring process, we welcome applications from individuals of all backgrounds, regardless of gender, age, disability, religion, ethnicity, or sexual identity, ensuring a rich and diverse collaboration environment.

In preparing for your interview, focus on your understanding of symbolic regression as a method of creating mathematical models. You can explain how it helps in developing interpretable equations from data, highlighting its utility in engineering applications such as metal fatigue assessment, where traditional models may fall short due to limited or variable data.

When answering this question, detail your programming experience, specifically any projects or coursework where you utilized Python. Highlight relevant libraries or tools you've used, such as NumPy or Pandas, and mention any specific applications of your programming skills in mathematical modeling or machine learning.

Address potential challenges such as data limitations, ensuring the robustness of numerical optimizations, and the need for clear interpretability of your findings. Emphasize your ability to tackle these challenges through careful planning and methodical research.

Use the STAR method (Situation, Task, Action, Result) to explain your experience with collaboration. Highlight your communication skills, ability to organize tasks, and contribution to achieving team objectives, linking it back to how such experiences would benefit you in the Master Thesis position.

Discuss the significance of explainable AI in promoting trustworthiness and understanding in engineering contexts. Point out how knowing the underlying rationale of a model's predictions can help engineers make informed decisions, especially when working with critical data like material fatigue assessments.

Answer by mentioning specific sources like academic journals, online courses, or professional conferences. Discuss any particular areas of interest and how you analyze new developments relevant to your field, ensuring that you remain knowledgeable about evolving technologies and methods.

Share your insights into numerical optimization, outlining common techniques like gradient descent and their relevance in training data-driven models. Also, discuss how optimizing parameters can affect the accuracy and performance of machine learning algorithms, especially in critical applications such as metal fatigue assessment.

Communicate your passion for blending technology and engineering. Emphasize your curiosity about solving real-world problems and how this Master Thesis aligns with your career goals of contributing to the engineering field through innovative technologies.

Outline your systematic approach to problem-solving, including identifying the problem, gathering relevant data, analyzing potential solutions, and implementing strategies. Emphasize your adaptability and willingness to learn from setbacks to achieve research objectives.

Express your goals, such as developing a robust new methodology for metal fatigue assessment and contributing meaningful insights to the field. Highlight your ambition for personal and professional growth and how you hope to foster collaborative relationships during your time at Bosch.