Do you want to bring artificial intelligence into technical applications? In collaboration with a team of engineers and scientists, you will investigate how to develop more robust and reliable predictive models for technical systems. You will work on enhancing a machine-learning toolbox to forecast vibration-loaded systems and add crucial capabilities to learn from real-world insights, especially when measurement data is scarce.

During your thesis you will research and apply advanced machine learning techniques to integrate limited measurement data into the training of models that currently rely predominantly on simulation data.

You will develop a benchmark by integrating simulated data and new measurement data from a test bench, utilizing machine learning algorithms to predict the dynamic behavior of nonlinear coupled vibration systems.

Furthermore, you will apply and evaluate your chosen approaches, comparing their model performance (accuracy and robustness) against simulation-only trained models.

Finally, you will openly communicate your ideas and contributions, benefiting from the exchange with colleagues within your team, experts in the field, and a broader network across various domains and locations within the company.