About this role
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 Contact: Sarah Disch