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 to 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.