Vacancies: Deep Learning in Medical Image Analysis

The Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center, Nijmegen, has vacancies for several PhD student or Postdoctoral positions.


Imaging is a central part of medicine. Computers can potentially extract more information from images, more reliably, and more accurately than human readers. This is exemplified by recent results in deep learning, currently the most active research area within machine learning and computer vision, and the topic of over 300 mostly recent papers in medical image analysis.

The Domain Applied and Engineering Sciences of the Dutch science foundation NWO has funded a large programme on Deep Learning in Medical Image Analysis. Sixteen researchers at five Dutch academic centers will work, in close collaboration with seven companies on various challenges that currently limit applicability of deep learning: high dimensionality of the data, availability of labeled data, and sensitivity to differences in acquisition protocols.

In our group we have four fully funded positions for research on detection and quantification of lung disease in chest computed tomography, detection of breast cancer on mammograms and breast tomosythesis, analysis of multiple types of abnormalities in chest radiographs, and retinal image analysis with fundus photography and optical coherence tomography. Collaborating companies are Thirona, ScreenPoint and Delft Imaging Systems. We closely work with clinicians in all projects, and have already collected large data sets with expert annotations. A team of scientific programmers is supporting our deep learning research and we have a large cluster of computers equipped with high-end GPUs for large scale experimentation.


You should be a creative and enthusiastic researcher with a MSc/PhD degree in Computer Science, Data Science, Physics, Engineering or Biomedical Sciences or similar, with a clear interest in machine learning, deep learning, image analysis and an affinity with medical applications. Good communication skills and expertise in software development, preferably in Python/C++, are essential. Experience with deep learning is a pre.

Terms of employment

Positions can be filled by either PhD students or postdocs.

PhD positions has the standard salary and secondary conditions for PhD students in the Netherlands. Your performance will be evaluated after 1 year. If the evaluation is positive, the contract will be extended by 3 years. The research should result in a PhD thesis. See also this page with some general information about doing a PhD in our group.

Postdoc positions are for three years. Your performance will be evaluated after 1 year. If the evaluation is positive, the contract will be extended by 2 years.


The Diagnostic Image Analysis Group (DIAG) is a research division of the Department of Radiology and Nuclear Medicine of the Radboud University Medical Center Nijmegen. Nijmegen is 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.

The focus of the Diagnostic Image Analysis group is the development and validation of novel methods in a broad range of medical imaging applications. Research topics include image analysis, image segmentation, machine learning, and the design of decision support systems. Application areas include neuro, breast, prostate, lung and retina imaging and digital pathology. Key to the success of the group is close cooperation with clinicians. Currently the group consists of around 40 researchers. Check our deep learning paper overview to get an idea of our work in this field.


To apply for the PhD positions, follow this link.

To apply for the postdoc positions follow this link.

If these links do not work, please send applications as a single pdf file to

In this pdf file the following should be included: CV, list of followed courses and grades, letter of motivation, and preferably a reprint or link to your Master or PhD thesis and publications in English you have written.

For further information contact Bram van Ginneken.