A postdoctoral position is available to work in the Epidemiology and Modelling Group of the Department of Plant Sciences at the University of Cambridge led by Professor Chris Gilligan to work on parameter estimation for epidemiological models to analyse the spread and control of plant diseases across a variety of real-life systems, including crop and natural systems in developed and developing countries.
The post: We are seeking a Bayesian statistician and epidemic modeller to build on our work to analyse the spread and control of plant pests and diseases in natural and agricultural settings. The research is intended to inform policy at a national and international level to aid in optimal surveillance to predict the future spread of and management of emerging diseases in developed (UK and US) countries and low and middle income countries, especially in sub-Saharan countries, with which the Epidemiology and Modelling Group has ongoing interactions. The successful individual will work with the current team of modellers to optimise and expand existing statistical inference approaches and will have unique opportunities to inform the implementation of national and international disease management policies, as well as for publication. Initial work will focus on wheat rust, cassava virus diseases and maize lethal necrosis in East Africa.
Qualifications: Applications are invited from candidates with a PhD in Bayesian statistics or a related subject. Practical experience of Makov chain Monte Carlo or Approximate Bayesian Computation is highly desirable and familiarity with statistical inference and mathematical modelling of plant disease epidemics would be an advantage. Knowledge of C++ and R is essential and knowledge of Python or similar language would be an advantage.
The ideal candidate will be able to work closely with epidemiological modellers and with field-based stakeholders involved in the collection of data for national pest and disease surveys and to advise stakeholders on the implementation of disease management strategies. Good communication skills are essential as the post holder will need to work well in a team and collaborate widely including communicating modelling results effectively to specialists and to non-technical clients and policy agencies. The post-holder will be based in the Epidemiology and Modelling Group at Cambridge where there is substantial expertise in modelling and parameter estimation.
Project details: Bayesian statistical inference provides a powerful means of estimating critical epidemiological parameters for the dispersal, transmission and control of plant disease in agricultural and natural environments. The successful applicant will work across multiple research projects supported by a variety of funders including the Bill and Melinda Gates Foundation, BBSRC, DfID and USAID. Initial work will focus on major pest and disease threats to staple crops in Sub-Saharan Africa.
Examples of key challenges that the post-holder will contribute to, include: - Estimation of transmission and dispersal parameters for stochastic epidemiological model, using extensive, patchy and incomplete surveillance data;
Allowing for meteorological driving-variables in making inferences from disease spread data;
How can we make predictions of the future spread of emerging plant disease epidemics?
How can we identify which strategies are most likely to reduce the risk of future spread?
How can the results of such simulations be best communicated to key stakeholders?
Once an offer of employment has been accepted, the successful candidate will be required to undergo a health assessment.
To apply online for this vacancy, please click on the 'Apply' button below. This will route you to the University's Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.
Please send a copy of your full CV with publications list, a supporting statement and contact information for two referees.
The funding for this post is available for two years in the first instance.
Please quote reference PD11811 on your application and in any correspondence about this vacancy.
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