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PhD position – Pest and disease modelling in grain cropping systems

Understanding pest and disease dynamics within farming systems and the ability to relate incidence and seasonal conditions to grain yield loss is a clear and present challenge for the agricultural science community. Currently, management of many pests, diseases and weeds in crops relies on an estimated economic damage threshold. The threshold is used to help identify when a pest population should be reduced to prevent yield loss.

However, this approach does not consider the environment, the timing of pest control in relation to crop development, the pest lifecycle or the economic cost to future crops. Further, in many farming systems, the decision to control a pest or disease outbreak is required before the economic threshold is reached, in which case identifying and forecasting drivers for epidemics can support and improve the outcomes of pest and disease control decisions.

In contrast to traditional reactive approaches, this project will develop a proactive forecasting approach, providing end-users with the ability to plan and holistically manage pest populations well before any economic damage thresholds are attained.

Funding

The successful applicant will be awarded with a Commonwealth RTP scholarship valued at approximately $27,500 AUD/year (tax-free) for 3.5 years.

Eligibility

  • The candidate must have an Honours Class 1 or equivalent and an IELTS score of 6.5 or greater.
  • The candidate will be required to relocate to Brisbane, Australia, for the duration of the PhD.

Questions may be directed to:

Dr Matthew Harrison
Senior Scientist
Tasmanian Institute for Agriculture, University of Tasmania
Australia
Matthew.harrison@utas.edu.au

Additional information on the project can be found here.

Scientific Project Officer – Crop yield forecasting with machine learning

The Food Security Unit (European Commission – Joint Research Centre, Ispra, Italy) is opening a Contract Agent position on machine learning and deep learning for yield forecasting in Africa using Earth observation and meteorological data.

The successful candidate will develop yield forecasting methods using machine learning and compare them, in terms of accuracy and timeliness, against current state of the art (mostly multiple regressions using remote sensing and meteorological data). The position is for one initial year (renewable up to six) and open for citizens of EU member states and associated countries.

More information on eligibility, the application process, and contact details.