Vacancies 66162-Postdoctoral-researcher-for-deep-learning-in-computational-pathology

  • 36 hours a week
  • Temporary
  • 1 year with a possible extension to 3 years
  • Date of publication: 6 March 2019
  • Deadline: 3 April 2019
  • Scale 11: max € 70984 gross per year at full employment (incl. vacation bonus and end of year payments)
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Job description

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 KWF-funded PROACTING project we will develop deep learning algorithms that will leverage a large amount of data consisting of histopathology pathology 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.

Profile

For this project we are 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.

Organization

The Computational Pathology Group (CPG) is a research group of the department of Pathology of the Radboud University Medical Center. CPG works closely together with the Diagnostic Image Analysis Group (DIAG) of the Department of Radiology and Nuclear Medicine. We develop, validate and deploy novel medical image analysis methods, usually based on machine 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.

Radboudumc
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.

Read more about what it means to work at Radboudumc and how you can do your part.

Employment conditions

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.

Comments and contact information

All additional information about the vacancy can be obtained from Dr. Francesco Ciompi. Use the Apply button to submit your application.

Please apply before 3 April.
Recruitment agencies are asked not to respond to this job posting.

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