A postdoctoral position in Atmospheric Physics and Meteorology is available in the Epidemiology and Modelling Group of the Department of Plant Science at the University of Cambridge led by Professor Chris Gilligan to work on the development and testing of meteorologically-driven, epidemiological models of emerging disease in crops and natural ecosystems.
We are seeking a modeller to build on our work in collaboration with the UK Met Office, to analyse how wind-borne, plant pests and diseases spread in natural and agricultural settings. The post-holder will be primarily responsible for utilising the Numerical Atmospheric Modelling Environment (NAME), developed by the UK Met Office to simulate the large-scale, long distance dispersal of plant pests and pathogens. The modelling is intended to inform policy at national and international levels for developing and developed countries to aid in optimal surveillance to predict the future spread of emerging epidemics and to compare management scenarios. The successful candidate will work with a team of modellers to optimise and expand current modelling approaches. There will be opportunities to inform the implementation of national disease management policies, as well as for scientific publication.
Applications are invited from candidates with a PhD in atmospheric physics, applied mathematics, physics, meteorology or a related subject. Familiarity with, meteorological modelling or atmospheric dispersal models would be an advantage. Knowledge of C++ or Fortran and a generic scripting language (such as Perl, Python or R) is highly desirable. The ideal candidate will be able to work closely with biologists to incorporate realistic biological processes into the models. Good communication skills are essential as the post holder will need to work well in a team and collaborate widely, including communicating results effectively 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 mathematical modelling and parameter estimation.
Fixed-term: The funds for this post are available until 31 December 2018 in the first instance.
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