PhD candidate 'Radiomics and distributed data-mining to develop oncological prediction models'

Radboudumc and MAASTRO collaborate in building predictive models in the field of radiation oncology. The aim of the project you will be working on is to develop oncological prediction models that use available patient data to predict the outcome and toxicity of the different treatment options. Among others, you will apply radiomics analysis of the imaging data (CT, MR etc.) to extract relevant parameters to further improve the accuracy of the predictions. Using these models both patient and doctor will be enabled to choose the optimal treatment for the patient.

Radboudumc aims to be at the forefront of the development of innovative, sustainable and affordable healthcare. Our mission is 'to have a significant impact on healthcare'. We believe we can achieve that by providing excellent quality, participatory and personalized healthcare, operational excellence and by working together in sustainable networks. The starting point for this is patients and their quality of life. Throughout all this, patient care, research and education go hand in hand.To realize our mission, we are searching for colleagues who want to take on this challenge with us; employees who are excellent in their field of expertise and give it their all by pushing boundaries and providing 'that little bit more'. At Radboudumc, you gain the confidence, receive and take responsibility to successfully make these changes. For the best patient care and the best future of healthcare.

Job description:
In order to build accurate prediction models, a large amount of data of already treated patients is needed. This implies that data from different hospitals, even different countries, needs to be combined to enable unified building and validation of the oncological models. The aim of the project you will work on is to build and use an IT infrastructure that makes this possible.Your activities will be to contribute to building an IT infrastructure that facilitates data sharing and data extraction among different (geographical) sites and between different datasets.This will entail creating state of the art IT technology such as building ontologies to make data interpretable in a uniform manner and making them universally accessible by using semantic web technologies and adapting them to data mining and model building in the field of oncology and radiomics. Using this infrastructure you will combine the data from the different institutes to make models that will predict the effectiveness of a certain treatment for an individual patient. You will work in an international team and will collaborate with institutes in the Netherlands (Erasmus MC, NKI etcetera) but also internationally (Moffitt / USF (USA) and Tianjin (China)).

You have a Msc degree in computer science, (applied) physics, mathematics, biomedical engineering or a comparable degree and you have extensive experience in programming and image analysis. Prior knowledge of statistical techniques such as machine learning is desirable. You are attracted to working on clinical problems and you like working in a (multidisciplinary) team.

Terms of employment:
  • You will have a combined appointment at both the Radboudumc and MAASTRO. The project is supported by a grant from STW (14930 : Strategy);
  • Appointment will be for a maximum of four years; Salary is € 2.244,- gross per month in the first year up to a maximum of € 2.874 gross per month in the last fourth year, plus additional vacation bonus (8% per year) and end of year payments (8.3% per year);
  • Upon commencement of employment we require a certificate of conduct (Verklaring Omtrent het Gedrag, VOG). Radboud university medical center’s HR department will apply for this certificate on your behalf.

Additional information about the vacancy can be obtained from Dr. René Monshouwer via or Prof. André Dekker via (Use these mail addresses only for information).

Application can be done via:
Applicants should send a letter of intent outlining special interest in the position, overall related qualifications, experience and career goals, a curriculum vitae, MSc transcript and grades, and names and addresses of professional references. 

Please apply before September 1, 2017.

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