Edinburgh posts postdoctoral role focused on transmission genetics and immune variation
The University of Edinburgh is seeking a Postdoctoral Research Associate to work on the genetic and immune determinants of host infectiousness, with the project centered on how individual differences shape pathogen transmission. The appointment sits within CMVM, the Roslin Institute, and Quantitative Biology, placing the post squarely in the interface of genetics, epidemiology, and computational biology.
This is not a bench-only post and not a pure statistics role. The project demands both biological insight and technical fluency, because the central scientific problem is how infectiousness varies across individuals and how those differences can be measured, modeled, and predicted. In practical terms, the postdoctoral researcher will help disentangle the genetic and immune influences on transmission while also accounting for evolutionary constraints that may limit or shape those traits over time.
The study system is Drosophila melanogaster, a long-established model for infection, immunity, and behavior. Using fruit fly data may sound distant from clinical medicine at first glance, but the logic is familiar: transmission is rarely driven by a single pathogen or a single host factor. Host genotype, immune response, and behavior can interact in ways that amplify or suppress spread, and those interactions are exactly what this project is built to examine.
A single host can differ from another in susceptibility, shedding, and contact behavior without any visible outward marker.
Computational modelling sits at the center of the appointment
The successful candidate will be expected to bring advanced analytical and computational modelling skills to the project. The role calls for genetic-epidemiological inference and prediction models, along with the ability to apply those tools to project data in a way that generates new scientific insight on disease transmission.
The advertised technical background is unusually specific. A strong candidate should have a PhD, or be within three months of completion, in a discipline with a substantial quantitative component such as quantitative genetics, statistics, bioinformatics, theoretical biology, physics, or applied mathematics. Experience in quantitative genetics, genomics, generalized linear mixed models, Bayesian statistics, MCMC, and mathematical modelling of disease or other dynamic biological processes is explicitly sought.
High-performance computing is also part of the job, especially efficient code for genetic and statistical data analysis and simulation studies. That emphasis tells you where the bottlenecks lie. Modern transmission research does not stop at estimating a few correlations; it depends on repeated simulation, parameter exploration, and rigorous model fitting across large or complex datasets. Without robust computing skills, even strong biological hypotheses can stall at the analysis stage.
Host infectiousness is the scientific target, not just infection itself
What makes this post distinctive is its focus on infectiousness as a trait. Many studies ask whether a host becomes infected; fewer confront the harder question of how much an infected host contributes to onward spread. That distinction matters in transmission biology because control strategies depend not only on who gets sick, but also on who drives chains of infection.
By examining the genetic and immune determinants of individual variation in infectiousness, the project aims to move closer to a mechanistic view of transmission. Evolutionary constraints are part of the picture as well. Traits that influence infection and spread do not exist in a vacuum; they are shaped by trade-offs, selection pressures, and biological limits that can preserve variation in a population even when certain traits appear disadvantageous.
For researchers trained in quantitative biology, the appeal is obvious. The project offers a setting where genotype, phenotype, immunity, and behavior can be examined together rather than as separate silos. The modelling challenge is substantial, but so is the biological payoff: better inference about why some hosts transmit more than others, and which factors most strongly predict that variation.
The role favors independence, collaboration, and publication-ready output
The appointment is full-time at 35 hours per week, with a part-time option at 80 percent. It is fixed term for 24 months full time, with the possibility of a longer period if the role is taken part time through the contract end date of 31 May 2029. The salary range is listed at £41,064 to £48,822 per year, pro rata.
Candidates are expected to demonstrate strong written and verbal communication skills, including a track record of research outputs such as peer-reviewed publications and conference presentations. Independence matters here, but so does team science. The project is structured for someone who can manage their own analysis while contributing to a broader research group and communicating findings clearly enough to support publication and collaboration.
Experience with Drosophila or another model species is desirable, though not mandatory. Training and support will be provided for skills not fully covered by a single candidate’s background, which broadens the field beyond specialists in one narrow method or organism. The opening is therefore aimed at a researcher with serious quantitative capability and enough biological flexibility to work across genetics, immunity, and transmission modelling.
The practical question for applicants is straightforward: can they turn complex biological variation into a coherent inferential model? This post is built for someone who can do exactly that, while keeping sight of the larger epidemiological question of how disease spreads through populations.