The Experimental Ultrahigh Field Magnetic Resonance group at the Max Delbrück Center works on magnetic resonance imaging and magnetic resonance spectroscopy in preclinical, translational, and population-based research projects.

Our group operates magnetic resonance infrastructure up to 9.4 Tesla, including cryogenically cooled radiofrequency coils for high-sensitivity imaging and spectroscopy. This infrastructure is used for structural and functional magnetic resonance imaging, diffusion-weighted imaging, quantitative imaging, magnetic resonance spectroscopy, and the development of new imaging and post-processing approaches.

In addition to magnetic resonance methodology, we work with large imaging datasets from population cohorts. A particular focus is the NAKO Health Study, one of the largest population-based health studies in Germany. The Max Delbrück Center is one of the five magnetic resonance imaging sites of NAKO and contributes to the acquisition of standardized magnetic resonance imaging data for this cohort.

Our research therefore combines magnetic resonance imaging, neuroimaging, biomedical image analysis, and computational methods. In the context of neuroinflammatory diseases such as multiple sclerosis, we are interested in identifying structural brain changes and lesion patterns in large magnetic resonance imaging datasets.

Projects in the group range from magnetic resonance method development and image acquisition to data processing, image analysis, machine learning, and translational biomedical applications. The advertised project is particularly suited for students interested in computer vision, deep learning, medical image analysis, and large-scale neuroimaging data.

Typical candidate profiles include:

– Backgrounds in computer science, computational sciences, computer vision, remote sensing, data science, biomedical engineering, medical image analysis, physics, or related fields
– Interest or experience in image processing, segmentation, deep learning, or quantitative image analysis
– Programming experience, preferably in Python and familiarity with tools such as PyTorch, nnU-Net, or similar software is an advantage
– Interest in magnetic resonance imaging, neuroimaging, population cohorts, and translational biomedical research