Student trainee in Digital Transformation (Computer Vision) (m/f/d)

You support the team in developing and training computer vision models (CNN, YOLO) for automated fault detection on ceramic components. You are responsible for building and maintaining image processing pipelines including data annotation and agumentation. The implementation of ML experiments, model comparisons and hyperparameter tuning (MLflow, Databricks) is also part of your tasks. You document models and results according to internal standards. You will also participate in the processing and analysis of structured production and quality data (Python, pandas, scikit-learn). You have the opportunity to share patents and publications.

You are a student (w/m/d) in a study programme focusing on computer science, data science, machine learning, electrical engineering or in a comparable MINT field. You could already gain first practical experience with ML projects during your studies or internship. The safe handling of Python (NumPy, pandas, scikit-learn), Deep Learning Framework (PyTorch, TensorFlow/Keras) and Computer Vision (OpenCV or comparable libraries) is a matter of course for you. Experience with image processing or computer vision tasks, SQL basic knowledge and knowledge of working with real, unadjusted data sets is desirable. Object detection/segmentation (YOLO, Faster R-CNN) as well as git and versioned experiment workflows awaken your interest. You are a team player (w/m/d) and work in virtual teams. In both German and English you will always find the right words.

Flexible working hours Partnership work in the team Technical and personal development Business and employee events Canteen Mobile work possible Accessible by public transport