Job opportunity: Epidemiological Modeller, University of Cambridge


Prof. Chris Gilligan’s lab in the Department of Plant Sciences at the University of Cambridge is currently seeking a mathematical and computational modeller, with experience in Bayesian parameter estimation, to develop and adapt a modelling framework for a major disease of plantation crops for use at landscape and country-wide scales. A stochastic, spatially-explicit modelling framework has been developed and tested under US conditions and it is now being adapted for spread of a disease of citrus plantations in the EU and potentially too for sub-Saharan Africa (SSA). A prime aim of the modelling is to provide a tool to inform industry and policy/regulators on preparedness, surveillance and options for management of the disease in the event of an outbreak.

The disease, known as citrus greening, is caused by a bacterium that is transmitted by insect vector and human movement of plant material. The modelling framework involves integration of the crop dynamics for selected countries in the EU/SSA, together with pathogen, vector and grower dynamics as well as incorporation of meteorological variables. Applications are invited from candidates with a PhD in applied mathematics, physics, statistics or a related subject. A strong background in applied modelling of biological or related systems would be an advantage along with an understanding of Bayesian parameter estimation. Familiarity with mathematical modelling of disease epidemics would be a strong advantage. Knowledge of Python, R and handling of spatial data is highly desirable. The ideal candidates will be able to work closely with other modellers in the group and liaise with biologists to incorporate realistic biological processes into models. The funds for this post are available for 2 years in the first instance.

For further details or to apply for the position, visit the link on the University’s Job Opportunities site. The closing date is 2 July 2021.

Leave A Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.