Vacancy: Deep Learning in Neurotrauma Imaging

The Diagnostic Image Analysis Group (DIAG) of the Radboud University Medical Center, Nijmegen, has a vacancy for a PhD student or postdoc.

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Background

An external insult to the brain can cause different degrees of damage. This ranges from none to mild, to coma and death. Imaging plays an important role for diagnosis and treatment. In particular cerebral microbleeds, which are very small bleeds in the brain visible as black dots in magnetic resonance (MR) images are an important marker for the severity of trauma and may have prognostic value for the patient. However, the detection and quantification of cerebral microbleeds are time consuming and prove to be a challenge even for an experienced neuroradiologist.

In this project we are going to develop deep learning algorithms to automate these tasks. To this end you will also develop deep learning algorithms for the automated segmentation of the main anatomical structures in the brain in different types of MR images.

This is a unique and exciting project in which you potentially can make a real impact. Trauma image analysis is a largely unexplored field. You will be part of an international team of enthusiastic clinical experts from Antwerpen, Cambridge and Trondheim.

This is an ERA-NET Neuron project funded by the Netherlands Organisation for Scientific Research (NWO) and the Netherlands Brain Foundation and is part of a larger European consortium, which aims at the development of a new classification system for traumatic brain injury. A summary can be found here. As part of this consortium we have access to multi-center patient imaging data collected in the CENTER-TBI study for the development and validation of our methodology.

Requirements

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

Terms of employment

The position can be filled by either a PhD student or a postdocs.

The PhD position 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.

The postdoc position is for two years. Your performance will be evaluated after 1 year. If the evaluation is positive, the contract will be extended by 1 year.

Organization

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.

Application

PhD candidates, please apply here

Postdocs, please apply here

If these links do not work, send applications as a single pdf file to Rashindra.Manniesing@radboudumc.nl. 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.

This application remains open until the position has been filled. Applications are processed immediately upon receipt.

For further information contact Rashindra Manniesing.