- During your Master thesis, you will support an ongoing PhD research project focusing on the embedding of sensors in Glass Fiber Reinforced Polymer (GFRP) components.
- In FRP components, porosity is an important factor for quality.
- Last but not least, you will create a machine learning model to predict porosity in Resin Transfer Moulding (RTM) processes from data from embedded accelerometers.
- Education: Master studies in the field of Data Science, Informatics, Mathematics, Physics or comparable
- Experience and Knowledge: in data science/machine learning, time series analysis; programming skills in Python; preferably experience in PyTorch or comparable and Unix systems
- Personality and Working Practice: independent, solution-oriented, self-taught with analytical thinking and open to challenges
- Languages: good in German (preferred) or in English
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Master Thesis - Immenstadt, Deutschland - Robert Bosch GmbH
Beschreibung
Master Thesis - Data Analysis for Porosity Prediction in RTM Processes
Robert-Bosch-Straße 1, 87509 Immenstadt im Allgäu, Germany
Full-time
Robert Bosch GmbH
Company Description
At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people's lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.
The Robert Bosch GmbH is looking forward to your application
Job Description
Qualifications
Additional Information
Zusätzliche Informationen:
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?
Markus Münch (Functional Department)
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