Research Fellow in statistical phylodynamic modelling of virus transmission and evolution
College of Medicine and Veterinary Medicine/The Roslin Institute, University of Edinburgh
300
9652
UE07 £33,797-£40,322
College of Medicine and Veterinary Medicine/The Roslin Institute
Fixed term 35 hours per week
This is one of 2 research fellow positions associated with the same project
We seek a talented and dedicated scientist with a background in computational biology, statistics, phylogenetics and bioinformatics, to analyse experimental and field data for virus infections in chickens, and to help develop phylodynamic inference and models for the effects of vaccination on virus transmission and evolution.
The Opportunity:
This research positions is part of the international collaborative project: “Combined influence of imperfect vaccines, host genetics, and non-genetic drivers on virus transmission and virulence evolution” funded by the international Ecology and Evolution of Infectious Diseases (EEID) Programme.
This project will generate informative, high-resolution empirical data to monitor virus transmission and establish the role of genome variability on virulence evolution. The main tasks of the successful candidate will be to analyse chicken transmission experiment data, including infection data and viral sequence data; and to help develop the statistical and phylodynamic models to describe how transmission and viral evolution changes with hosts of different immune status or genetic background, in transmission experiments and in the field. This will help to develop strategies to control the ecology, evolution and economic burden of viral diseases in poultry and other species.
Your skills and attributes for success:
· Experience in advanced statistics applied to biological data and in computational and statistical bioinformatics techniques
· Experience in phylogenetics, ideally viruses or other fast evolving organism.
· Strong track record in publishing in internationally recognized scientific journals
· Ability to work effectively in a multi-disciplinary project team and presenting technical methods and results to non-quantitative audiences
Ref: 340
Closing date: 3 June 2021