Master Thesis Data-Efficient Hybrid Machine Learning for Robust Vibration System Prediction

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.

Do you have experience in Spark?, Do you have a Master's degree?, * Education: Master studies in the field of Engineering, Mathematics, Physics, Computer Science or comparable with good grades

• Experience and Knowledge: very good knowledge of Python (Pytorch, Pandas, Numpy etc.); good to very good knowledge of fundamental machine learning concepts and algorithms, particularly relevant for regression; good understanding of dynamics / mechanics
• Personality and Working Practice: you excel at driving innovation with a high degree of self-motivation, working independently while communicating your progress and ideas effectively
• Work Routine: your on-site presence is required
• Languages: fluent in English and basic in German or fluent in German and very good in English

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