Research Assistant / Associate (Ecology)

A Research Assistant / Associate position, funded by a Wellcome Trust Collaborative Award in Science, is available in the research group of Prof. Dylan Childs. The grant is led by Prof. Michael Brockhurst at University of Manchester, together with lead investigators Prof Dylan Childs at the University of Sheffield, Profs Steve Paterson and Joanne Fothergill at the University of Liverpool, and Prof James Chalmers at the University of Dundee. You will be sited within the vibrant research environment of the School of Biosciences but will work closely with the collaborative network.

This Collaborative Award is investigating the evolutionary mechanisms of resistance to ciprofloxacin during treatment for chronic lung infections caused by Pseudomonas aeruginosa. The overarching goal of the project is to discovering the mechanisms of resistance evolution and develop biomarkers that can predict which patients are at risk of developing resistance. Work at Manchester and Liverpool has focused on discovering the mechanisms of resistance in clinical isolates of Pseudomonas aeruginosa lung infections. This includes genomics and high-throughput phenotyping of the evolution of antibiotic resistance in patient samples.

The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung infection. As part of this work, the postholder will also explore the contribution of the host microbiome to resistance emergence.

(Research Associate) Applicants must have a PhD (or be working towards a PhD) in quantitative biology, statistics, or a related discipline, and have experience using statistical modelling and data analysis to address predictive or inferential questions in ecological or biological systems. Proficiency in implementing and interpreting statistical and ecological process models in R or Julia is essential.

(Research Assistant) Applicants must hold a BSc or MSc in ecology, quantitative biology, statistics, or a related discipline, and have experience using statistical modelling and data analysis to address predictive or inferential questions in ecological or biological systems. Proficiency in implementing and interpreting statistical and ecological process models in R or Julia is essential.

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