Master Thesis: Deep Learning for Defect Detection in Battery Cells
vor 4 Wochen
At Fraunhofer IPT, we are working on optimizing production processes for lithium-ion battery cells and fuel cells. To achieve this, we are developing a deep learning-based defect detection system. As part of your master's thesis, you will investigate different deep learning approaches for defect detection and implement them in the system.
Key Responsibilities:
- Selection of suitable deep learning approaches for defect detection
- Training and evaluation of deep learning models
- Implementation of deep learning models in the defect detection system
Requirements:
- Studies in mechanical engineering, computer science, or a comparable field
- First experience with PyTorch and deep learning is an advantage
- High degree of initiative, independence, and problem-solving skills
What We Offer:
- Ideal conditions for practical experience alongside your studies
- GPU server for data science applications and efficient model training
- Flexible working to combine study and job in the best possible way
We value and promote the diversity of our employees' skills and welcome all applications, regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation, and identity. Severely disabled persons are given preference in the event of equal suitability.
Fraunhofer plays a central role in the innovation process with its focus on developing key technologies vital for the future. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future.
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