- 36 hours a week
- 2 years with a possible extension
- Date of publication: 10 July 2019
- Deadline: 19 August 2019
- Scale 10A: max € 42220 gross per year at full employment (incl. vacation bonus and end of year payments)
- First interview scheduled: 21 August 2019
Job descriptionWe invite applications for a PhD project in the field of artificial intelligence, diagnostic image analysis and clinical sciences. This position is part of research project investigating application of cineMRI (a new dynamic MRI technique) in personalized treatment for patients with chronic pain after surgery. The project is embedded in the departments of Surgery (Dr. R. ten Broek, Prof. H. van Goor) and the Diagnostic Image Analysis Group (Dr. H. Huisman) at Radboud University Medical Center.
This project is funded bij a research grant from 'Maag Lever Darm Stichting'.
This project aims to improve outcomes in the 30.000 patients per year in the Netherlands that develop chronic pain after abdominal surgery. This pain is often caused by adhesions. A recent, imaging technique (CineMRI) can detect and localize these adhesions, resulting in improved decision making and outcomes. CineMRI is not widely used for detection of adhesion in the abdomen, because radiological reading is time-consuming and expertise dependent. Recent advances in Artificial Intelligence (AI) can help create tools that realize clinical implementation of CineMRI. Radboudumc is an expert center in the application of AI to medical image analysis using state-of-the-art Convolutional Neural Networks (CNN). We have an extensive database of CineMRI and abdominal MRI.
Tasks and responsibilities
- Research how AI can enhance CineMRI.
- Implement AI-enhanced CineMRI together with Watson Medical.
- Validate AI-enhanced CineMRI in a multi-center study.
ProfileYou are a creative and ambitious researcher with an MSc degree in Medicine, Computer Science, Data Science, Physics, Engineering or Biomedical Sciences or similar with a clear interest in machine learning, deep learning and medical image analysis. Good communication skills and a working knowledge in deep learning are essential.
OrganizationThe work is performed in the Surgery department and the Diagnostic Image Analysis Group (diagnijmegen.nl) a research division of the department of Radiology and Nuclear Medicine of the Radboud University Medical Center Nijmegen. DIAG research topics include image analysis, image segmentation, machine learning, and the design of decision support systems. Application areas include: neuro, breast, abdomen, lung and retina imaging and digital pathology. DIAG consists of 50 PhDs, several postdocs and 7 staff members.
The department of Surgery is a clinical department with strong focus on research and training. The department specializes in advanced colorectal, esophageal, and pancreatic surgery. Research is focused on evidence based surgery, and implementation of technological innovations.
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.
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Employment conditionsUpon 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 informationAll additional information about the vacancy can be obtained from Richard ten Broek, Post-doctoral researcher, +31 (0)6 363 043 10 and Henkjan Huisman, Associate Professor +31 (0)24 361 75 36. Use the Apply button to submit your application.
Please apply before August 19, 2019
Recruitment agencies are asked not to respond to this job posting.
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