PhD student position in machine learning / cancer biology

Universitair Medisch Centrum Groningen

Universitair Medisch Centrum Groningen

PhD student
Regio Groningen
Niet nader bepaald
Niet nader bepaald


Working environment

PhD student position in machine learning / cancer biology:
“Unraveling cancer biology using machine learning on big data”

We have an exciting opportunity for a PhD student. The position will be for 4 years and should result in a PhD thesis at the University of Groningen.
The PhD student position is available in the research group of dr. Rudolf Fehrmann. The research group is embedded in the Department of Medical Oncology of the University Medical Center Groningen (UMCG), where oncological patient care is coordinated with preclinical and fundamental research on the biology and treatment of cancer. The research group also participates in the Cancer Research Center Groningen (CRCG), which is part of the Graduate School for Medical Sciences. Within the graduate school, you have the opportunity to follow high-quality courses throughout your PhD training.
The research group is focused on bioinformatics / computational biology with big data in the context of cancer. Our goal is to identify driver genetic alterations, genes and biological pathways that are relevant for the pathophysiological behavior and treatment response of tumors. Strong collaboration exists with the research group of prof. dr. Marcel van Vugt that is focused on cell cycle regulation and DNA damage responses in the context of cancer. More information on current research topics can be found on our website:

Job description

You will work on a project aiming to unravel the ‘cancer-immune setpoint’ in cancers with machine learning. During cancer development, tumor cells undergo molecular ‘rewiring’ to escape the immune system, often by activating mechanisms that suppress an anti-cancer immune response. Immunotherapy can circumvent some of these mechanisms and thus trigger an anti-cancer response. Evidence is emerging that a complex set of tumor, patient and environmental factors govern the strength and timing of the anti-cancer immune response. The combined result is a ‘cancer-immune setpoint’, which has been defined as the equilibrium between the factors that promote or suppress this response. You will use natural language processing to obtain big-data data from the public domain (genomics and transcriptomics), implement auto-encoders to define molecular subclasses and build neural networks to predict relevant phenotypes for the cancer-immune setpoint.

What do we need

We offer a PhD studentship to an ambitious candidate with a passion for machine learning and cancer biology.
The candidate must be fluent in English, both spoken and written, and must be an enthusiastic, ambitious team player.

The UMCG has a preventive Hepatitis B policy. The UMCG can provide you with the vaccination, should it be required for your position.
In case of specific professions a ‘Certificate of Good Conduct’ is required.

What do we offer

A full-time contract (36 hours/week) for initially 4 year, after one year an evaluation is conducted.

Your salary will be a maximum of € 3,103 gross per month (scale PhD), depending on your qualifications and relevant experience, based on a full-time appointment.
In addition, the UMCG will offer you 8% holiday pay, an 8.3% end-of-year bonus.
The conditions of employment comply with the Collective Labour Agreement for Medical Centres (CAO-UMC). English:

More information
For more information about this vacancy you may contact dr. Rudolf Fehrmann, PhD, (for inquiries only, please do not use for applications).

Select Reading
Wind TT, Jalving M, de Haan JJ, de Vries EGE, van Vugt MATM, Reijngoud DJ, van Rijn RS, Haanen JBAG, Blank CU, Hospers GAP, Fehrmann RSN. A large pooled analysis refines gene expression-based molecular subclasses in cutaneous melanoma. OncoImmunology, 2019; 8: 1558664.

Moek KL, de Groot DJA, de Vries EGE, Fehrmann RSN. The antibody-drug conjugate target landscape across a broad range of tumour types. Ann Oncol. 2017; 28:3083-3091.

Bense RD, Sotiriou C, Piccart-Gebhart MJ, Haanen JBAG, van Vugt MATM, de Vries EGE, Schro¨der CP, Fehrmann RSN. Relevance of tumor-infiltrating immune cell composition and functionality for disease outcome in breast cancer. JNCI. 2017; 109:djw192.

Fehrmann RS et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat Genet. 2015. 47(2):115-25.


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You can apply until 1 September 2019.
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