Vacancy: Extracting imaging biomarkers for AMD with Deep Learning

The Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center, Nijmegen, is offering one PhD position for the project Foresight of blindness: Extracting imaging biomarkers for progression of age-related macular degeneration with deep learning

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Project Description

Age-related macular degeneration (AMD) remains the leading cause of blindness in the elderly, affecting more than 50 million Europeans. About 15% of affected patients progress to irreversible vision changes and, ultimately, blindness. Therapies to slow progression are becoming available, but clinicians are currently insecure to foresee who will progress and will need swift action to save sight. The goal of this project is to automatically extract reliable biomarkers from multimodal and longitudinal retinal images using deep learning that can predict AMD progression. How to effectively combine heterogeneous 2D and 3D data from different projections with deep learning architecture is a research question that will be addressed in this project, as well as the analysis of temporal evolution or the use of semi-supervised approaches when complete annotations are not available for some of the modalities.

The automatically extracted biomarkers will be combined with other biomarkers, such as genetic and environmental markers, into a prediction model that can assess the risk of progression to late AMD. This model will give clinicians the possibility to identify patients at high-risk of progression and to provide them active surveillance and personalized therapy to prevent blindness.

You will be part of a multidisciplinary team, consisting of machine learning and clinical researchers and will work closely with the Ophthalmology departments in RadboudUMC and ErasmusMC, as well as international groups and consortia.


We are looking for ambitious deep learning engineers, data scientists, or machine learning experts. You should be a creative, and enthusiastic and have an MSc/Ph.D. degree in Computer Science, Data Science, Physics, Engineering or Biomedical Sciences or similar, with a clear interest in deep learning, image analysis and medical applications. Good communication skills and expertise in software development, preferably in Python/C++, are essential. Experience with machine learning should be evident from the (online) courses you've followed, your publications, GitHub account, etc. Experience with medical image processing is preferred.

Terms of employment

You will be appointed as a PhD student with 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.


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 healthcare with over 8,500 staff and 3,000 students.

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 retinal imaging and computational pathology. Key to the success of the group is close cooperation with clinicians. Currently, the group consists of around 40 researchers. See our deep learning paper overview to get an idea of our work in this field.

We offer excellent research facilities with large data storage facilities, a cluster of 40 high-end GPUs which can be scaled dynamically to include cloud servers, and support from a team of research software engineers, data analysts, and radiologists.


To apply for this position please follow this link.

All applications will be processed immediately upon receipt until the position has been filled.

For further information contact Clarisa Sanchez.