Project description: The aim of the project is to use paired host-virus genomics to understand why patients respond differently to infections. We are sequencing host and virus genomes from large patient cohorts infected with HCV, HBV and HIV. These cohorts are very well characterised and many clinical phenotypes and biomarkers are measured on all individual. The aims of this study are (1) to identify host polymorphisms that drive evolution of the virus, (2) identify host and virus genetic polymorphisms that drive differences in clinical phenotypes and measured biomarkers independent of each other and (3) detect interactions between host and virus genetics that drive the differences in clinical phenotypes and measured biomarkers. The role can be focused on different aspects of the project depending on your interest and experience for instance on the host genomics and GWAS or on virus genomics, evolution and epidemiology. Depending on your experience you will be involved in development and implementation of new statistical approaches to look for interaction between host and pathogen genetic markers and associations (possibly nonlinear) with multivariate clinical outcomes.

Requirements: A PhD with a strong quantitative component, particularly population genetics, bioinformatics, computational biology, statistics or probabilistic machine learning, computer science or other relevant fields. Experience of working with large datasets is necessary. Computational skills to include experience of using statistical packages such as R, MATLAB or others. Experience of developing computational pipelines and analytical strategies for complex data sets, especially pathogens. Candidates must be able to express themselves in spoken as well as written English.

Desirable selection criteria: Experience of performing phylogenetics and phylogeographic analyses. An understanding of the genetics of infectious disease, in particular viral genomics. Understanding of concepts in genetics, in particular population genetics. Training in statistical modelling and inference. Understanding of Bayesian statistics. Low-level programming experience (for example, C++). Experience in processing and analysis of next generation sequencing data either DNA or RNA expression.

Instructions for the application: The application has to be made through the University of Oxford portal. The link is provided below:

Application deadline: 6 January 2022, if position is not filled we will re-post the position.

Type of employment: Full-time 3 years (part-time and flexible working hours will be considered).

Link for the advert: https://bit.ly/315WBt6

For further information about the position please contact: Dr. Azim Ansari, ansari.azim@ndm.ox.ac.uk