The Computational Cancer Biology lab of Martin Schaefer is looking for a predoctoral Fellow.
The project is related to biases in biological networks. Protein-protein interaction networks are key to understanding biological and disease processes, but current methods to detect, integrate and analyze those networks suffer from high technical error rates and biases, particularly due to the overrepresentation of frequently studied proteins. We recently published a study, in which we argue that the power law distribution in protein-protein interaction networks may result from testing some proteins much more frequently than others for interaction partners. One algorithmic solution to this problem is to avoid highly studied proteins and edges. One alternative idea is to use experimental information to reduce the bias in the networks itself. In this project we will use machine learning techniques to address those problems.
The successful candidate should hold a master in computer science, physics or computational biology and should have expertise in statistics, programming, network science and the analysis of cancer omics data. Knowledge of other data science techniques in particular machine learning and being familiar with concepts of tumor evolution are a plus.
Don't hesitate contacting us for informal inquiries by email. Application (which should include a CV, cover letter, a list of publications and contact details of 2 - 3 referees) should be sent through the appropriate link.
The fellowship is available immediately, the salary will be commensurate to the experience.
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Posted on 23rd September 2025