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. • Education: Master studies in the field of Engineering, Mathematics, Physics or comparable with good grades • Experience and Knowledge: good understanding of dynamics (mechanical vibrations) / mechanics, 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 • 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 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? Annika Hayn (Functional Department) Work #LikeABosch starts here: Apply now! #LI-DNI