PhD position: Next generation mucosal vaccines

WorkplaceZurich, Zurich region, Switzerland


ETH Zurich is one of the world’s leading universities specialising in science and technology. It is renowned for its excellent education, its cutting-edge fundamental research and its efforts to put new knowledge and innovations directly into practice. We are looking for an outstanding student with a microbiology, molecular biology, glycobiology or bioinformatics background to join an exciting project developing next generation mucosal vaccines.

Virus-like particles have proved highly effective for vaccination against a broad range of targets. Novel glycoengineering techniques now make it possible to assemble microbial glycans on virus-like particles. Owing to hugely increased production efficiency over classical glycoconjugate vaccines, these are cheap enough to be applied to livestock and can be produced in sufficient quantities for mucosal vaccination. Your first aim will be to study the effect of antibodies induced by these vaccines on pathogen evolution (largely focusing on E. coli). This will include mouse- and pig-infection models, next-generation sequencing, and molecular characterization of bacterial glycans. By combining this analysis over multiple strains and species, the overall aim is to develop in silico methods to predict "non-escapable" glyco-epitope combinations based on pathogen genome sequences. This has the potential to revolutionize the way in which anti-bacterial vaccines are designed and applied.
This project is a collaboration with the groups of Prof. Markus Aebi, ETH Zürich and Prof. Martin Bachmann, University of Bern. The starting date is open.

A strong background in microbiology, microbial ecology and evolution, synthetic biology and/or bioinformatics will be highly advantageous in this project. Excellent students interested in moving into this field from immunology, biochemistry, biophysics or related disciplines will also be considered.

In your application, please refer to
and reference  JobID 42723.

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