Your responsibilities

Develop quantitative models of gene expression providing mechanistic insights into perturbed proliferation and migration in cancer cells (see e.g., Strasen et al., Molecular Systems Biology 2018; Fritzsch et al., Molecular Systems Biology 2018; Bohn et al., PNAS 2023; Hartmann et al., Life Sci Alliance 2024)
Employ machine learning techniques and train kinetic models using quantitative experimental data (bulk and single-cell RNAseq, single-molecule RNA FISH, live-cell imaging and genome editing)
Conduct comprehensive data analyses, including transcriptomics, functional genomics and/or microscopy imaging
Collaborate closely with experimentalists and theoreticians within or outside the group
Participate in teaching in systems biology

Requirements

Master’s degree (or equivalent) in computational biology, bioinformatics, physics, mathematics, molecular biology, informatics, or a related field
Strong skills in programming and numerical computation, e.g., Matlab, Python, R or Julia
Enthusiasm for basic research questions and the ability to solve problems independently
Excellent communication skills and good team spirit
Fluency in spoken and written English
Experience in quantitative systems biology research is beneficial

Application procedure

Please send a single PDF file containing:

A motivation letter including a description of previous research experiences
A full CV
A list of publications (if applicable)
Scans of academic degrees (e.g. Abitur & Master degree)
Contact details of two referees

Submit your application to bewerbersystembio@ibmg.uni-stuttgart.de.

Starting date

As soon as possible

Closing date

Applications will be handled immediately but must be submitted by 15th April 2026. Informal inquiries should be addressed to Stefan Legewie (bewerbersystembio@ibmg.uni-stuttgart.de).

The University of Stuttgart has been certified as a family-friendly university since 2012 (http://www.uni-stuttgart.de/uniundfamilie/index.en.html) and wishes to increase the share of female academic staff and, for this reason, especially welcomes applications from women. Severely challenged applicants will be given preference in the case of equal qualifications.

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