PhD position available



Collaborating Institutes:

  • The Netherlands Cancer Institute (NKI), Amsterdam.
  • Delft University of Technology (TUDelft), Delft
  • VU University Medical Center, VUMC, Amsterdam
  • The Johns Hopkins University School of Medicine, Baltimore, USA.


Project description. In the Netherlands, 7600 women are diagnosed yearly with an ovarian tumor. Only about 5% of these tumors are malignant. It is very important to accurately identify these malignant tumors prior to surgery to ensure proper treatment. Unfortunately, this can be very challenging. In this project, we aim to find specific molecular biomarkers in liquid biopsies that can efficiently discriminate between benign and malignant ovarian tumors with a sensitivity of at least 90% and a specificity of at least 80%. We will determine genetic abnormalities (mutations, copy number aberrations and chromosomal abnormalities) in circulating tumor DNA and RNA profiles of tumor educated platelets (TEPs). We will develop novel computational approaches to analyze these unique datasets separately and jointly, to arrive at accurate predictors to identify malignant tumors from liquid biopsies.


Interdisciplinary collaboration. The groups of Lodewyk Wessels and Marcel Reinders and (NKI and TUDelft) will develop advanced machine learning approaches to construct (integrative) predictors from the different data streams. The supervisory team will be strengthened by the pathology expertise of Hugo Horlings (NKI) and collaborations with Victor Velculescu at Johns Hopkins (circulating copy number profiles from circulating DNA) and Tom Wurdinger at the VUMC (TEPs). The project is embedded within broad oncological collaborations within the VUMC, LUMC, Catharina Ziekenhuis in Eindhoven and the NKI. The successful candidate will be employed at the NKI and will spend at least two days per week at the TUDelft. Regular meetings between project partners will be held to generate sufficient cohesion and momentum.


Candidate profile: We are seeking a highly motivated PhD candidate with:

  • A degree in bioinformatics, computer science or a related discipline
  • Experience in statistics, machine learning and/or pattern recognition
  • Proficiency in bioinformatics programming languages (e.g. R, Python)
  • Good cross-disciplinary collaborative and communication skills
  • Experience in analysing high-throughput molecular data is a plus
  • Experience in cancer biology and clinical applications is a plus


Interested? Please send your CV and motivation letter to Lodewyk Wessels ( Please include at least two references.


Deadline: 01 September 2020