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. Starting date is 1 March 2022. Working at Radboud university medical center means that you are ahead of the curve and working together on the healthcare of the future. And there is more. Our secondary terms of employment are impressive. These are fully tailored to you thanks to our Employment Conditions Selection Model. At Radboud university medical center, you will be given trust, and you will take the responsibility to handle everything together. We provide annual courses, both professional and personal.
- In addition to your monthly salary and an annual vacation allowance of 8%, you will receive an end-of-year bonus of 8.3%.
- If you work irregular hours, you will receive an allowance.
- As a full-time employee (36 hours per week), you are entitled to approximately 168 vacation hours (over 23 days) per year.
- Radboud university medical center pays 70% of the pension premium. You pay the rest of the premium with your gross salary.
- You get a discount on health insurance as well: you can take advantage of two group health insurance plans. UMC Zorgverzekering and CZ collectief.
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.
Radboud university medical center is a university medical center for patient care, scientific research, and education in Nijmegen. Radboud university medical center strives to be at the forefront of shaping the healthcare of the future. We do this in a person-centered and innovative way, and in close collaboration with our network. We want to have a significant impact on healthcare. We want to improve with each passing day, continuously working towards better healthcare, research, and education. And gaining a better understanding of how diseases arise and how we can prevent, treat, and cure them, day in and day out. This way, every patient always receives the best healthcare, now and in the future. Because that is why we do what we do.
Read more about our strategy and what working at Radboud university medical center means. Our colleagues would be happy to tell you about it. #weareradboudumc For these projects the Computational Pathology Group of the Radboud University Medical Center, Nijmegen (The Netherlands), is seeking a Postdoctoral researcher with experience in development of deep learning models.
You should be a creative and enthusiastic researcher with a PhD in a relevant field, such as medical image analysis, computer vision, or deep 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 in Python, as well as expertise in deep learning model development using Tensorflow or Pytorch are essential.
All additional information about the vacancy can be obtained from Dr. Jeroen van der Laak, associate professor Pathology. Use the Apply button to submit your application.