Thesis: Towards AI-Assisted Annotation of Industrial Image Datasets

Summer Semester 2027 – limited to 3–6 months

YOUR TASKS:

• Work with large-scale image datasets from real-world industrial applications to support the development of high-performance AI models.
• Investigate existing data annotation methods and develop automated annotation pipelines to make the current semi-automated, iterative, and time-consuming process more efficient.
• Develop solutions for novel industrial use cases where only a limited amount of labeled training data is available.
• Investigate and evaluate one or more of the following approaches as part of your thesis:
  • Active Learning
  • Ensemble Learning
  • Model Distillation
  • Synthetic Data Generation
  • Hyperparameter Optimization

YOUR PROFILE:

• Currently pursuing a degree in Artificial Intelligence, Computer Science, Mathematics, or a comparable field
• Good command of English
• Programming skills in Python and PyTorch, as well as experience working with REST APIs
• Knowledge of Deep Learning, Computer Vision, and Hyperparameter Optimization
• Independent, structured, and analytical approach to work
• Interest in scientific research and innovative solutions in the field of Artificial Intelligence
• Creativity and enthusiasm for exploring and implementing new methods in practice

Information at a Glance

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