Diagnostic pathology involves microscopic evaluation of human tissues. Increasingly, microscopic images are digitized to support the diagnostic workflow. This rapidly growing field of digital pathology also yields ample opportunities for development of computer-aided diagnosis algorithms. State-of-the-art deep learning methods have recently been proven capable of supporting the diagnostic work of pathologists and we have now reached the point where such algorithms can be implemented in a routine clinical setting. Furthermore, deep learning approaches have the potential to extract relevant information for the design of predictive and prognostic biomarkers, e.g., tumor-infiltrating lymphocytes, tumor-stroma ratio, etc.
Currently, we are executing a project that studies the use of deep learning for detection of Serous Tubal Intraepithelial Carcinoma (STIC), which is a precursor for ovarian cancer development. A second project focuses on deep learning for breast cancer grading (i.e. assessing the aggressiveness of the tumour), for which a large cohort of patients was already collected in a previous study. The latter project is aimed to result in a prototype algorithm which will be further productized and certified through collaboration with a commercial party.
- You should be a creative and enthusiastic researcher with a PhD degree in a relevant field, such as medical image analysis, computer vision, or machine learning.
- You should have a clear interest to develop image analysis algorithms and an affinity with medical topics.
- Good communication skills
- Expertise in software development, preferably in Python, are essential.
- A challenging position in one of Europe's largest research groups for Computational Pathology. We strive to create a stimulating work environment with an eye for your personal development.
- 2-year contract of 36 hours per week (full time). Starting date: 1rst of March 2022
- Salary scale 10, min € 2911 - max € 4615 gross per month (based on a full time position)
- An annual vacation allowance of 8% and you will receive an end-of-year bonus of 8.3%
- 168 vacation hours (over 23 days) per year (based on a full time position)
- 70% coverage of the pension premium by Radboudumc. You pay the rest of the premium with your gross salary
- A discount on health insurance: you can take advantage of two group health insurance plans. UMC Zorgverzekering and CZ collectief.
The Computational Pathology Group (CPG) is a research group of the department of Pathology of the Radboud University Medical Center (Radboudumc). We are also part of the cross-departmental Diagnostic Image Analysis Group (DIAG) at Radboudumc, with researchers in the departments of Radiology and Nuclear Medicine, Pathology, and Ophthalmology.
We develop, validate and deploy novel medical image analysis methods, usually based on deep learning technology and focusing on computer-aided diagnosis (CAD). Application areas include diagnostics and prognostics of breast, colon, prostate and lung cancer. Our group is among the international front runners in the field, witnessed for instance by the highly ssuccessful Camelyon and Panda grand challenges which we organized.
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.
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.
All additional information about the vacancy can be obtained from Dr. Jeroen van der Laak. Use the Apply button on this page to apply for this position. This vacancy closed on October 26.