Immunotherapy with checkpoint inhibitors has importantly changed perspectives for many metastatic melanoma patients. Still, the majority of patients do not derive long-term benefit from this treatment. Currently, no single biomarker is available that can reliably predict who will and who will not respond to immunotherapy. If non-response could be predicted, patients could be spared the potential severe side effects of immunotherapy and start a more effective treatment. Aim of this study is to develop machine learning algorithms based on clinical data and histological images from primary melanomas of 1500 immunotherapy- treated metastatic melanoma patients that can predict response to immunotherapy.
You will be a key player in a research team with oncologists, pathologists and computational scientists collaborating with two other PhD students. You will play an important role in the collection and analysis of clinical and melanoma pathological image data and developing of a machine learning algorithm to predict immunotherapy-response.
The UMC Oncology department aims to continuously improve the outcome of patients with cancer. Innovative research is performed based on strong collaborations between basic, translational and clinical scientists.
You are an ambitious MSc or MD with a strong interest in clinical research. You should hold a degree or have expertise in bioinformatics, computer science or a related discipline, have experience in statistics and/or machine learning. You are a team-player with strong communication skills.
The maximum salary for this position (36 - 36 hours) is € 4.615,00 gross per month based on full-time employment.
In addition, we offer an annual benefit of 8.3%, holiday allowance, travel expenses and career opportunities. The terms of employment are in accordance with the Cao University Medical Centers (UMC).
If you have any questions about this vacancy, please contact Karijn Suijkerbuijk, Internist-Oncoloog, phone number: 06-55234706, e‑mail adress: K.Suijkerbuijk@umcutrecht.nl.
Acquisition based on this jobopening is not appreciated.