Master thesis: »Defect detection with transformer models«

  • Praktikum
  • Aachen

Webseite Fraunhofer-Institut für Produktionstechnologie IPT

The Fraunhofer-Gesellschaft ( currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 30,000 employees work with an annual research budget of 2.9 billion euros.

CHATGPT is on everyone’s lips in artificial intelligence for Natural Language Processing (NLP). The reason for this are Transformer Models, which lead to these impressive results due to their new architecture. However, Transformer Models are not only used for NLP, but also for Machine Vision tasks. Although they are significantly newer than Convolutional Neural Networks (CNN), they already achieve state-of-the-art results in benchmarks. Your task in the master thesis will therefore be to apply a state-of-the-art Deep Learning Transformer model for defect detection on the coating surface of the lithium-ion battery anode and to compare the results with a state-of-the-art CNN model.

What you will do

  • Research on the current state of transformer models for object detection tasks
  • Implementation of a Transformer Model for an existing defect dataset
  • Comparison of the results of the Transformer Model with an existing CNN Model

What you bring to the table

  • You are studying mechanical engineering, computer science, CES or a comparable subject
  • Experience in Deep Learning is required
  • A high degree of initiative, independence and motivation
  • Good language skills in German and/or 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

Questions according to this position will be answered by:
Alexander Kreppein M.Sc.
Research assistant production quality
Phone: +49 241 8904-289

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