Master Thesis Machine Learning-Based Magnetic and Mechanical Properties Prediction of E-Sheet Metal Materials
Master Thesis Machine Learning-Based Magnetic and Mechanical Properties Prediction of E-Sheet Metal Materials
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Job Description
Edrive components such as stator and rotor have high requirements regarding mechanical and magnetic properties. Due to future recycled content requirements chemical properties are expected to change. The effect of this changed chemical composition on the mechanical and magnetic properties will be described using a data based approach.
- In your Master thesis you will extend an existing mechanical ML model to include data useful features and methods for magnetic properties.
- Your job includes data mining for data that meet the required criteria.
- Furthermore you will develop a combined datadriven model of mechanical and magnetic properties.
- Last but not least you will validate and extend existing and new ML methods with Python.
Qualifications :
- Education: Master studies in the field of Mathematics Computer Science Physics Engineering or comparable
- Experience and Knowledge in data processing and analysis; programming skills (Python with e.g. scikitlearn)
- Personality and Working Practice: you structure your work efficiently; your strong communication skills and sense of responsibility make you a valuable team player
- Enthusiasm: welcome to share your Git project with us
- Languages: very good in 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
Dominic Olveda (Functional Department)
49 2
Tatjana Miokovic (Functional Department)
49 7
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
