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
Post-Doc in Machine Learning in Biomedicine (E13 TV-L, 100%)
starting as soon as possible. The initial fixed-term contract will be for 2 years with possible extension.
The group has extensive knowledge at the interface between statistical machine learning, digital medicine, and computational biology. 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, Scheid et al. 2016, Nature, Döring et al. 2016, Retrovirology, Caskey et al. 2017, Nature Medicine).
Nico Pfeifer is one of the PIs of the excellence cluster proposal “Machine Learning: new perspectives for science” that is currently under review and he is also associated faculty of the International Max Planck Research School for Intelligent Systems.
The University seeks to raise the number of women in research and teaching and therefore urges qualified women academics to apply for these positions. Equally qualified applicants
with disabilities will be given preference. The employment will be carried out by the central administration of the University of Tübingen.
The ideal candidates will have a Ph.D. or equivalent in Machine Learning, Data Science, Biometry, Biostatistics, Bioinformatics, Computer Science, Computational Biology or a related life science discipline. The applicants should have an interest in interdisciplinary work. Experience in data science and machine learning as well as strong programming/scripting skills (Java, C/C++, R, Matlab, Python, JavaScript) are required as well as a strong publication record. Other relevant qualifications include:
• Background in Statistics
• Experience with medical data (clinical data, molecular data, …)
• Experience with high-throughput data (next-generation sequencing, mass spectrometry)
• Databases (MySQL, NoSQL)
• Privacy-preserving machine learning
Knowledge of the adaptive immune system or infectious diseases is a plus.
Please send your application (including motivation letter, curriculum vitae, transcripts and certificates, and contact details of two academic references) via e-mail as a single PDF to nico.pfeifer [at] uni-tuebingen.de (subject: Post-Doc application (Machine Learning in Biomedicine)) by April 3rd.