To reinforce our team in Lausanne, Switzerland, we are seeking a Post-doc.
Post-doc in statistical genetics


University of Lausanne (UNIL)

Work Location

Lausanne, Switzerland

Employment fraction


Type of contract


Job tasks
Background: The Statistical Genetics Group of Zoltán Kutalik at Unisanté and the Department of Computational Biology at the University of Lausanne is investigating the interplay and the genetic architecture of complex human traits and diseases. In particular, we develop robust causal inference methods tailored to discover links between risk factors and complex diseases. We are doing so by jointly modelling the genetic architecture and the complex causal network of human traits. Additional research interests include: investigating gene-environment interactions, causal impact of molecular phenotypes (methylome, transcriptome, proteome, metabolome), copy-number variant associations etc. Via longstanding collaborations, we are fortunate to have access to several large cohorts (including the UK Biobank) with various omics data. We have an extensive web of collaborations both in Switzerland and abroad (University of Bristol, University of Exeter, University of Edinburgh, University of Regensburg, etc.). Our group is also member of the Swiss Institute of Bioinformatics.

The project involves developing new context-dependent causal inference methods in space and time. The candidate will be principally analysing data from the UK Biobank. The position is funded by the Swiss National Science Foundation (FNS) and the initial contract is for 1 year (renewable for up to 4 years in case of mutual agreement). Remuneration will be based on the salary scales applied by the FNS, typical annual salary is ~84’000 CHF.

Tasks: The candidate will spend at least 75% of her/his activity on personal scientific research and will be responsible for/participate in:
  • developing novel methodologies and their software implementation
  • collaborate with other international teams
  • performing GWAS / Mendelian Randomisation studies
  • teaching

Profile requirements
We are interested in recruiting talented and highly motivated individuals with an academic degree (PhD) in statistics / mathematics / bioinformatics / computer science. The ideal candidate should have:
  • strong background in statistics
  • good programming skills (R, Python preferred, C++ is a plus)
  • past experience with solving biological problems (especially experience with genome-wide association studies) using computational tools
  • good communication skills and excellent command of English (French is a plus)
  • Phd degree (statistics / mathematics / bioinformatics / computer science or equivalent).

How to apply
If you are interested in this challenging and highly interesting position, please send your application including CV and letter of motivation by email to until 30 September 2022.