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 (CAD) 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.
In the PROACTING project (funded by the Dutch Cancer Society (KWF)) we will develop deep learning algorithms that will leverage a large amount of data consisting of histopathology images and corresponding (weak) labels, with the aim of building computer systems that can assist pathologists and oncologists during cancer diagnostics and personalized (neoadjuvant) treatment procedures. This project will be performed in close collaboration with the Molecular Pathology research group of Jelle Wesseling at the Netherlands Cancer Institute.
For this project 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. This is an excellent opportunity to develop cutting-edge deep learning technology to have an impact on breast cancer research and personalized cancer treatment.
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.
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.
The Computational Pathology Group 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 successful CAMELYON16 and CAMELYON17 grand challenges which we organized. The clinical partner in the PROACTING project is the Dutch National Cancer Institute (Nederland Kanker Institute, NKI) in Amsterdam, which is the biggest cancer research center in the Netherlands.
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Send applications to Dr. Francesco Ciompi, assistant professor in computational pathology. Applications will be reviewed on an ongoing basis until the position is filled.
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