PhD position on biomedical image registration

PhD position on biomedical image registration


Image registration is the process of aligning two or more images to achieve pointwise spatial correspondence. This is a crucial step in many medical image analysis application as it enables the integration of previously unrelated data, allowing for joint processing of the information. By aligning images from different modalities, complementary information can be fused or propagated from one modality to another. For example, morphological information from a CT image can be fused with functional information from a PET image. Furthermore, image registration can be used to track the progression of the disease overtime such as it is done for follow-up assessment of cancer patients. By establishing spatial correspondences between multiple images, it aligns anatomical structures and facilitates the automatic quantification of findings in subsequent imaging examinations. This saves radiologists a significant amount of time while ensuring accurate and efficient patient care.

Job description

The PhD project aims to develop a generic image registration framework that is capable of automatically configuring itself to be applied to several tasks. The main focus of the PhD is on the technical development of appropriate methods and requires a certain mathematical understanding. However, the methods developed are to be applied in the field of medical image processing and in particular in the field of oncological treatment assessment.

Tasks and responsibilites

  • Conduct research in the development of image registration methods
  • Collaborate with a multidisciplinary team to translate cutting-edge technology into clinical practice
  • Publish research findings in peer-reviewed journals and present at international conferences
  • Mentor junior team members and contribute to the development of the lab's research direction
  • Co-organize the international Learn2Reg image registration challenge and be active in the research community


You should - be a creative and enthusiastic researcher with an MSc degree in Computer Science, Mathematics, Physics, Engineering or similar - have a clear interest to develop artificial intelligence algorithms and an affinity with healthcare - have good communication skills and enjoy working in a multidisciplinary team - have expertise in software development, preferably in Python - having experience with deep learning, machine learning, and image analysis or time series analysis is a plus.

Terms of employment

You will be appointed for four years as a PhD student with the standard salary and secondary conditions for PhD students in the Netherlands. The research should result in a PhD thesis.


You will work in the Diagnostic Image Analysis Group (DIAG) DIAG is part of the Departments of Imaging, Pathology, Ophthalmology, and Radiation Oncology of Radboud University Medical Center. We develop computer algorithms to interpret and process medical images. The group currently consists of around 70 researchers. Radboud University Medical Center and Radboud University are located in Nijmegen, the oldest Dutch city with a rich history and one of the liveliest city centers in the Netherlands. Radboud University has over 17,000 students. Radboud UMC is a leading academic center for medical science, education and health care with over 8,500 staff and 3,000 students.


For more information please contact Alessa Hering by e-mail.


Use the Apply button on this page to apply for this position. You should supply - motivation letter - your CV including links to a Google Scholar profile - list of grades and courses you have followed including online courses on deep learning and similar topics, - links to any publications you have written - Any code you have written and is publicly accessible, e.g., on a GitHub account. This application will remain open until the position has been filled. Applications are processed immediately upon receipt.


Alessa Hering

Alessa Hering

Assistant Professor

Bram van Ginneken

Bram van Ginneken

Professor, Professor, Scientific Co-Director