Master Thesis Deep Learning for Multi-Channel Vision in Product Manufacturing
Location: Renningen, BW, de
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
Apply now to Bosch Research and combine academic excellence with real-world industrial impact. In this Master thesis, you will contribute to the development of advanced anomaly detection methods for industrial multi-channel vision systems for quality assessment in series production. Our goal is to design a generalizable deep-learning model that leverages vision features to reliably detect defects across varying products and manufacturing scenarios.
- During your Thesis you will explore cutting-edge approaches in anomaly detection and multi-channel image processing to develop a novel, generalizable model for quality assessment in industrial production.
- Furthermore you will implement, train, and evaluate your approach using real-world production data, ensuring its robustness and suitability for industrial applications.
- Lastly you will work closely with experts from research and development as well as production, regularly presenting your results and thus fostering continuous innovation in computer vision for manufacturing.
Qualifications
- Education: advanced studies in the field of Computer Science, Machine Learning, Artificial Intelligence or comparable
- Experience and Knowledge: excellent machine-learning fundamentals and very good programming skills in Python and in one deep learning framework, e.g. PyTorch or JAX
- Personality and Working Practice: you are able to approach complex tasks in a structured and analytical manner, you have a high motivation to learn and work independently on challenging topics, always communicating your results clearly and understandably
- Work Routine: we offer you the possibility to work remotely part of the time
- Languages: very good in English
Additional Information
Start: according to prior agreement
Duration: 6 - 8 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?
Matthias Kayser (Functional Department)
+49 711 811 40982
Petru Tighineanu (Functional Department)
+49 711 811 13878
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