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The Chair for Methods in Medical Informatics (Prof. Dr. Nico Pfeifer), Department of Computer Science at Eberhard Karls University Tübingen, one of eleven German universities distinguished as excellent under the German government’s initiative, is currently looking for a


Ph.D. Student in Biomedical Data Science (HCV) (E13 TV-L, 65%)


starting as soon as possible. The initial fixed-term contract will be for 2 years with possible extension. The position is funded by the German Center for Infection Research (DZIF).


The prescription of direct-acting antiviral agents is associated with high rates of sustained virological response (SVR) in patients treated against hepatitis C virus (HCV). Treatment failure is associated with the presence of resistance-associated mutations (RAMs), which can emerge during treatment or be transmitted. The use of genotypic drug resistance tests such as geno2pheno[hcv] or geno2pheno[ngs-freq], which identify RAMs in the amino-acid sequences of the non-structural (NS) proteins NS3, NS5A, and NS5B, can guide treatment decisions. The successful candidate will use statistical methods to analyze next-generation sequencing (NGS) data and apply and extend state-of-the-art machine learning methods to further improve upon current genotypic systems.


The group has extensive knowledge at the interface between statistical machine learning, digital medicine, and computational biology. Nico Pfeifer is a PI in the excellence cluster “Machine Learning: New Perspectives for Science” starting in January 2019. We are developing methods that allow answering new biomedical questions (Speicher and Pfeifer 2015, Proceedings of ISMB/ECCB 2015) and optimize them in close contact with our excellent national and international biomedical partners (Carlson et al. 2016, Nature Medicine, Schoofs et al. 2016, Science, Döring et al. 2016, Retrovirology, Mendoza et al. 2018, Nature).



The ideal candidate will have an M.Sc. or equivalent in Biomedical Data Science, Biometry, Biostatistics, Bioinformatics, Medical Informatics, Computer Science, Computational Biology or a related life science discipline. Applicants should have an interest in interdisciplinary work. Experience in data science and machine learning as well as strong programming/scripting skills (C/C++, R, Matlab, Python, JavaScript, Java) are desirable. Other relevant qualifications include:

  • Background in Statistics

  • Experience with medical data (clinical data, molecular data, …)

  • Experience with high-throughput data (next-generation sequencing)

  • Databases (MySQL, NoSQL)

Knowledge of the adaptive immune system is a plus.


In case of equal qualification and experience, physically challenged applicants are given preference. The University of Tübingen aims at increasing the share of women in science and encourages female scientists to apply. Candidates will be officially employed by the administration of the University of Tübingen.


Please send your application (including motivation letter, curriculum vitae, transcripts and certificates, and contact details of two academic references) via e-mail to
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Application deadline: December 21st, 2018.

Candidates are encouraged to send their application material early since we will start reviewing applications already before the deadline.