Bt/mt: »deep Learning-based Defect Classification
Vor 4 Tagen
In order for fuel cells to be used on a mass scale, manufacturing costs must be reduced. One way of achieving this is through efficient roll-to-roll processes. However, errors occur over time, which must be detected within the roll-to-roll process. For this purpose, we are looking for a motivated student to develop a Deep Learning (DL)-based defect detection and classification system.
**What you will do**
- Creation and preparation of image data in cooperation with the process experts
- Implementation of a DL-based model for defect detection and classification
- Comparison of DL-& ML-based methods with classical machine vision approaches
- Validation and documentation of the results
**What you bring to the table**
- Experience in the field of Machine Learning / Deep Learning is an advantage
- A high degree of initiative and team spirit
- Very good language skills in German and English
**What you can expect**
- Ideal conditions for practical experience alongside your studies
- Professional supervision and collaboration in a dedicated team
- A state-of-the-art machine park equipped with edge cloud systems and 5G infrastructure
- Flexible working to combine study and job in the best possible way
With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.
**Questions according to this position will be answered by**:
Alexander Kreppein M.Sc.
Research assistant Automated Machine Learning
Phone: +49 241 8904-289
Fraunhofer Institute for Production Technology IPT
Requisition Number: 64775 Application Deadline:
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