Postdoctoral Research Associate in Disease Transmission Genetics
Investigate genetic and immune drivers of pathogen transmission using Drosophila models and computational modeling.
Best for:Best for quantitative geneticists or computational biologists with strong Bayesian statistics and high-performance computing skills.
Overview
The University of Edinburgh's Roslin Institute seeks a Postdoctoral Research Fellow to join a quantitative biology project investigating the genetic, immune, and evolutionary determinants of host infectiousness. Funded by a prestigious research grant, the role utilizes Drosophila melanogaster as a model system to study pathogen transmission dynamics. The successful candidate will apply advanced analytical, statistical, and computational modeling approaches to genetic-epidemiological data. Responsibilities include conducting simulation studies, developing predictive models, and publishing scientific findings in peer-reviewed journals.
What you'll do
- Apply genetic-epidemiological inference and prediction models to project datasets using advanced analytical methods.
- Develop and execute efficient computational code for genetic and statistical data analyses and simulation studies on high-performance computing systems.
- Investigate the genetic and immune determinants of host infectiousness and pathogen transmission using Drosophila melanogaster.
- Collaborate with team members to generate scientific insights into the evolutionary constraints and drivers of disease spread.
- Prepare manuscripts for publication in peer-reviewed scientific journals and present research findings at academic conferences.
Requirements
Degree required: PhD or near completion of a PhD (within 3 months) in a quantitative discipline
Language: English; excellent verbal and written communication skills
Instruction: English
Publications: Track record of research outputs, including publications in peer-reviewed scientific journals.
- Proven research track record in quantitative genetics and genomics
- Experience with generalised linear mixed models and Bayesian statistics or MCMC
- Background in mathematical modeling of disease or dynamic biological processes
- Proficiency in high-performance computing and writing efficient computational code for data analysis
- Demonstrated track record of research outputs including peer-reviewed publications and conference presentations
- Open to international applicants worldwide who hold or are close to completing a relevant PhD.
Funding & terms
How to apply
Deadline: 2026-11-13
Submit your application through the external portal by the specified closing date. Ensure all requested qualifications and background details are addressed in your submission materials.
- CV
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