Deciphering the drivers of dry land crop production plasticity under a variable and rapid changing climate for Australia

Project opportunity

This Earmarked Scholarship project is aligned with a recently awarded Category 1 research grant. It offers you the opportunity to work with leading researchers and contribute to large projects of national significance.

Accurate and timely production estimates are essential to Australia’s grain producers and industry to better deal with downside risks caused by climate extremes and market volatilities. To address this complex issue, it is important to understand the economic and climatic drivers causing the large fluctuations in crop production across farming businesses of Australia.

The primary focus of this PhD position is to undertake functional research in the exploration, analysis, and disentangling of the mechanisms (e.g, markets, enviro-types) and their causalities on crop production systems across time and space. Thus, leading to a quantitative predictive fused model with increased accuracy and lead time for crop production estimates across Australia. This will be done by developing and application of hybrid Bayesian statistical and artificial intelligence (AI) approaches to determine crop yield, area, and production for main winter and summer crops across Australia. This PhD is part of the four-year project funded and supported the ARC LP-funded “CropVision: A next-generation system for predicting crop production”, a project led by UQ QAAFI.

Scholarship value

As a scholarship recipient, you'll receive: 

  • living stipend of $32,192 per annum tax free (2023 rate), indexed annually
  • tuition fees covered
  • single Overseas Student Health Cover (OSHC)


Associate Professor Andries Potgieter

Queensland Alliance for Agriculture and Food Innovation


Preferred educational background

Your application will be assessed on a competitive basis.

We take into account your

  • previous academic record
  • publication record
  • honours and awards
  • employment history.

A working knowledge of applied bayesian statistics, ML software, and libraries such as TensorFlow and/or Pytorch would be of benefit to someone working on this project.

A background or knowledge of crop physiology, environmental or remote sensing sciences or closely related sciences and the design and application of multi-modal data fusion algorithms to support systems modelling is highly desirable.

Latest commencement date

If you are the successful candidate, you must commence by Research Quarter 2, 2023. You should apply at least 3 months prior to the research quarter commencement date.

If you are an international applicant, you may need to apply much earlier for visa requirements.

How to apply

You apply for this project as part of your PhD program application.

View application process