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  3. Postdoc 'Innovative methods for personalized prediction based on data from rare cancers'

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Postdoc 'Innovative methods for personalized prediction based on data from rare cancers'

We offer an exciting postdoc position for a (mathematical) statistician or statistical physicist to develop methodology in the field of personalized …

6 maanden geleden


Geert Grooteplein Zuid, Nijmegen, Gelderland
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
32 - 36 uur
€ 2911 - € 4615 per maand


We offer an exciting postdoc position for a (mathematical) statistician or statistical physicist to develop methodology in the field of personalized prediction, based on data from rare cancer patients and in particular from patients with salivary gland cancer. The project research group consists of researchers from biostatistics, statistical physics and medical oncology. Will you join us?

We aim to find a researcher who is interested in developing the new mathematical methodology, as well as applying them to cancer data in collaboration with researchers from oncology. The position offers the opportunity to build a scientific path in an area of important societal interest and potential impact as it supports improving treatments for patients suffering from rare cancers.

In research into rare cancers only small data sets are typically available. When using such small data sets for prediction, the risk of overfitting in the models used is high. This limits severely the ability to infer statistically significant patterns in these data, patterns which might have suggested novel treatments. Pooling data from different institutions could alleviate the situation, but is in practice challenging due to regulatory and logistical problems. The project is financed by the Hanarth Fund.

You will work on two complementary routes for confronting the challenges of small data sets for rare cancers.
  • The first is to focus on more powerful techniques for inference that are better able to cope with small sample sizes, without overfitting.
  • The second route is to design and improve machine learning algorithms that circumvent the need for data pooling at one location for analysis by `cycling' around medical data repositories with small data sets (federated learning).
Data on salivary gland cancer patients will be analyzed with the proposed methods.


You have a PhD degree in (mathematical) statistics, statistical physics or a related field. Furthermore you have scientific interest in medical applications, and in particular in rare cancers. Knowledge of the field of survival analysis will be an advantage. You also have well-developed social skills directed to working in multidisciplinary teams.


Working at Radboud university medical center means that you are ahead of the curve and working together on the healthcare of the future. And there is more. Our secondary terms of employment are impressive. These are fully tailored to you thanks to our Employment Conditions Selection Model. At Radboud university medical center, you will be given trust, and you will take the responsibility to handle everything together. We provide annual courses, both professional and personal.
  • In addition to your monthly salary and an annual vacation allowance of 8%, you will receive an end-of-year bonus of 8.3%.
  • If you work irregular hours, you will receive an allowance.
  • As a full-time employee (36 hours per week), you are entitled to approximately 168 vacation hours (over 23 days) per year.
  • Radboud university medical center pays 70% of the pension premium. You pay the rest of the premium with your gross salary.
  • You get a discount on health insurance as well: you can take advantage of two group health insurance plans. UMC Zorgverzekering and CZ collectief.
In addition to our terms of employment, we also offer employees various other attractive facilities, such as childcare and sports facilities. Want to learn more? Take a look at the CAO UMC.

Additional information

All additional information about the vacancy can be obtained from Dr. Marianne Jonker, Assistant Professor Health Evidence or from Prof. dr. Ton Coolen, Full Professor. Use the Apply button to submit your application.