Nerve Fiber Trajectories, Reconstructed From Diffusion MRI
Visualization of Uncertainty in Inferred Nerve Fiber Directions

PhD Position on Visualization of Propagator-Based Diffusion Imaging Data

The Visualization and Medical Image Analysis group is looking for a highly motivated PhD student who is interested in working on a DFG-funded research project on the visualization of propagator-based diffusion imaging data.

Diffusion MRI

By measuring the heat motion of water molecules in living tissue, diffusion MRI provides a unique insight into its microstructure, offers early indicators of disease or tissue regeneration, and enables in-vivo mapping of the major nerve fiber bundles in the brain.

For computer scientists, the complex and noisy data generated by this imaging modality continues to pose exciting challenges that range from fundamental theoretical work on mathematical diffusion modeling, to the development and evaluation of robust algorithms for tractography and connectivity mapping, and the detection of abnormal diffusion properties that may indicate disease.


Visualization uses graphical representations of data, and frequently combines them with semi-automated mathematical analysis and/or tools for interactive exploration, to gain understanding and insight. Often, visualization is an indispensable key for the analysis of large data that is too complex for an exhaustive and reliable fully formalized analysis. One example of a field that generates such data is modern neuroimaging, including diffusion MRI.

Application Requirements

The applicant's profile will preferably include all or most of the following characteristics:

  • Master of Science (or equivalent) in computer science, physics, mathematics, or other related fields
  • strong interest and, ideally, research or work experience in image or signal processing, scientific visualization, mathematical modeling, statistics, or related fields
  • good written and oral communication skills (English)
  • good programming skills (C/C++, OpenGL, Python)

The position is available immediately, and will remain open until a suitable candidate has been found.

Applicants should send their CV, transcript of records, and a short motivation letter, to Prof. Thomas Schultz.