Postdoc in Cognitive Neuroimaging: Relating space, time and number processing in the human brain (1.0 FTE)
Ongeveer 19 uur geleden - Universiteit Utrecht - Utrecht
A large amount of data of already treated patients 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 ...
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).
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 Rene.Monshouwer@radboudumc.nl or Prof. Andre Dekker via email@example.com (Use these mail addresses only for information. Use the Apply button to submit your application.)
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.
The Radboudumc center advances human knowledge by conducting biomedical, translational and clinical research in order to improve wellbeing.
Our key strength is medical life-sciences and clinical practice, with an impressive infrastructure comprising state-of-the-art technology platforms and (translational) research facilities. The Radboudumc is therefore uniquely positioned in the emerging Euregio and Dutch healthcare infrastructure to play a leading role in the new healthcare paradigm of prediction, prevention and personalised medicine.
The Radboudumc focuses on scientific health challenges of today, with an eye on emerging diseases of the future.
Our mission: ‘to have a significant impact on healthcare’.
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.
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