Job Description
1. Do you want to bring artificial intelligence into technical applications? In collaboration with a team of engineers and scientists, you will investigate the probabilistic prediction of technical system behaviour. You will work on a Machine Learning Toolbox to forecast vibration-loaded systems and add additional features.
2. You will develop a benchmark by integrating simulated data and measurement data from a test bench, utilizing a Machine Learning Algorithms to predict the dynamic behaviour of nonlinear coupled systems.
3. Furthermore, you will compare and evaluate various machine learning approaches, primarily focusing on neural networks, within an existing pipeline using simple examples and measurement data. Subsequently, you will assess your approach in terms of accuracy and robustness.
4. You will use a Python-based framework and implement both data-based and analytical approaches.
5. Last but not least, you will communicate your ideas and contributions enthusiastically and benefit from the exchange with colleagues.
Qualifications
6. Education: master studies in the field of Engineering, Mathematics, Physics, Computer Science or comparable with good grades
7. Experience and Knowledge: very good knowledge of Python (pytorch, pandas, numpy etc.); good knowledge of machine learning; basic knowledge of dynamics / mechanics
8. Personality and Working Practice: communicative, innovative and self-motivated person, who is able to work independently
9. Languages: business fluent in written and spoken German or English
Additional Information
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)
+49 711 811 30652
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