Master Thesis in Data-Driven Force Prediction for Rotary Grinding in the Semiconductor Industry

You will be part of an innovative research project where data-driven methods meet cutting-edge technology. Ready to turn your ideas into impact? Apply now and help shape future solutions. • You will conduct a comprehensive review of existing force modeling methods for rotational grinding and explore state-of-the-art machine learning approaches for sequence-to-sequence regression on time-series data, such as Symbolic Regression, Convolutional Neural Networks, and Mamba. • As part of the data analysis process, you will analyze and prepare the provided time-series data, including feature engineering to extract relevant physical parameters. • Based on your research, you will implement and train various machine learning frameworks for force prediction. • To assess the developed approaches, you will create and apply a robust comparison matrix using key performance indicators such as prediction accuracy (RMSE, MAE), computational cost, and interpretability. • Throughout the project, you will regularly document, present, and discuss your findings and progress with the project team. • Education: master studies in the field of Mechanical Engineering, Data Science, Computer Science, Physics, or comparable • Experience and Knowledge:  • strong programming skills in Python • familiarity with common data science libraries such as Scikit-learn, TensorFlow/PyTorch, and Pandas • Personality and Working Practice: you are highly self-motivated and enjoy tackling challenging research topics independently • Work Routine: your on-site presence is required • Languages: fluent in English and very good in German 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? Jonathan Hilberg (Functional Department) Work #LikeABosch starts here: Apply now! #LI-DNI