ImFusion's machine learning models are powering many medical imaging applications. Automatic segmentation of anatomical structures enables clinicians to easily plan and safely execute interventional procedures.

We are looking for a motivated and talented working student to join our Anatomy Plugins group. Our goal is to achieve high-quality automatic segmentation of various anatomical structures (spine, head, heart, ...), which requires large and high-quality datasets. We also need to be able to evaluate the quality of machine-learning models and have robust evaluation pipelines to measure progress.

The working student will focus on the following tasks:

• Curate and improve datasets from publicly available sources and in-house data using data science and our state-of-the-art software tools.
• Create automated and semi-automated reports on the quality of the data and model predictions.
• Define Python-based workflows to analyze, detect, and correct issues in existing datasets.
• Think creatively about how to enhance and automate the labeling process and the integration of new datasets.
• Contribute to improving the machine learning models in our anatomy plugins that are used in actual medical devices.

The working student will have the opportunity to contribute to the improvement of our plugins and gain valuable experience for a future career as a data scientist.

This is a paid working student position and will take place in our main office (Munich, Germany) and remotely.

Shortlisted candidates will be contacted for a technical interview consisting of a Python programming exercise and a scientific discussion.

Join us to contribute to groundbreaking research in medical imaging and work in an internationally renowned research and development company!

Please apply by January 31, 2026.