Vacancies 85181-PhD-candidate-Deep-learning-for-Prostate-MRI-Surveillance
  • 36 hours a week
  • Temporary
  • 4 years
  • Date of publication: 16 July 2020
  • Deadline: 1 September 2020
  • Scale 10A: min € 2495 - max € 3196 gross per month at full employment (excl. vacation bonus and end of year payments)
apply to job

Job description

The PhD candidate will work at DIAG and at MAGIC. DIAG currently has 50 deep learning researchers focused on various medical image analysis topics. The focus of DIAG 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. A team of scientific programmers is supporting our deep learning research, maintaining a large cluster of computers equipped with high-end GPUs for large-scale experimentation. 

One million men receive a prostate cancer diagnosis, and three hundred thousand die from prostate cancer each year worldwide. Magnetic Resonance Imaging (MRI) represents a major breakthrough by accurately detecting clinically significant prostate cancers at an early, potentially more curable stage. The same MRI can also be used for better treatment targeting and for avoiding unnecessary systematic biopsies. As a result, the demand for Prostate MRI is rapidly increasing. Unfortunately, reading these multiparametric MRI (mpMRI) exams is difficult and requires substantial expertise. Computers can potentially extract more information from mpMRI, more reliably, and more accurately than human readers. Artificial Intelligence and more specifically Deep Learning is becoming indispensable in helping improve mpMRI diagnostic performance.

This EU funded project aims to research deep learning computer-aided diagnostic (DL-CAD) that will demonstrably help clinicians to get the best possible prostate cancer diagnosis from mpMRI. Radboudumc is a clinical expert on prostate MRI and technical expert in the field of prostate AI technology for over 20 years and an early adopter of deep learning in medical imaging. This project is a continuation of previous research in which CAD achieves expert performance in evaluating single time point mpMRI. In this project we will focus on surveillance mpMRI that has the potential to further reduce unnecessary biopsies and reduce over-treatment. For this project we are seeking a PhD candidate.


A creative and enthusiastic researcher with an excellent MSc degree in a relevant field, such as medical image analysis, computer vision, or machine learning. The ambition and academic skills to write and present scientific papers. Solid experience with deep learning and good programming is essential and should be evident from the (online) courses you've followed, projects you've done, and/or your GitHub account. Experience in developing/exploring medical image analysis algorithms and a strong affinity with medical topics.


The Minimally Invasive Image-Guided Intervention Center (MAGIC) has its roots in MRI-guided prostate treatment, and we have expanded to CT-, US-, and MR- image guided oncologic interventions, as for example biopsy of prostate, bone and liver; (non-) thermal ablations of pancreas, liver, kidney, prostate and vascular malformations. Supplementary we focus on nuclear interventions with Holmium particles; intra-operative navigation; robotics; ex-vivo imaging; clinical decision support; and perfusion and vascular interventions. The interventions are developed and performed in close collaboration with the Medical Innovation and Technology expert Center (MITeC).

The Diagnostic Image Analysis Group (DIAG) and Minimally Invasive Image-Guided Intervention Center (MAGIC) are research divisions of the Department of Imaging 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 healthcare with over 8,500 staff and 3,000 students. The research is carried out in strong collaboration with radiologists at the department of Radiology and EU partners (AI specialists and expert clinicians) in the EU project.

Research Institutes
At the moment there are more than 1,300 PhD candidates at our medical hospital. This number includes PhD candidates on our pay roll as well as external candidates (those employed somewhere else but researching on our premises).
  • Radboud Institute for Health Sciences: ± 700
  • Radboud Institute for Molecular Life Sciences: ± 400
  • Donders Center for Medical Neurosciences: ± 200
Read what it is like to do a PhD at the Radboud University Medical Center.

Radboudumc strives to be a leading developer of sustainable, innovative and affordable healthcare to improve the health and wellbeing of people and society in the Netherlands and beyond. This is the core of our mission: To have a significant impact on healthcare. To get a better picture of what this entails, check out our strategy.

Read more about what it means to work at Radboudumc and what our people have to say about it.

Employment conditions

Upon commencement of employment we require a certificate of conduct (Verklaring Omtrent het Gedrag, VOG) and there will be, depending on the type of job, a screening based on the provided cv. Radboud university medical center’s HR Department will apply for this certificate on your behalf.

Read more about the Radboudumc employment conditions and what our International Office can do for you when moving to the Netherlands.

Comments and contact information

In your application, please include a motivation letter, your cv, a list of grades, links to your publications, and your Master thesis or other works you have written in English.

All additional information about the vacancy can be obtained from Dr. Ir. Henkjan Huisman, associate professor Radiology and Nuclear Medicine. Use the Apply button to submit your application.

Please apply before 1 September.
Recruitment agencies are asked not to respond to this job posting.

apply to job

more information

Working at Radboud university medical center

  • Read more about Terms & Conditions of employment.

    read more