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

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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 cre…

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