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Parametrising of AI architecture for enhanced canopy trait prediction for wheat utilising high temporal, spatial and spectral hypercubes.

This project is closed.

Project summary

Program
PhD
Location
St Lucia
Research area
Agricultural, veterinary and food sciences, Mathematical sciences

Project description

This research optimizes AI models to enhance the accuracy and robustness of wheat canopy trait prediction from hyperspectral data.
UQ co-invests in grains research including via a new initiative, Analytics for the Australian Grains Industry (AAGI) which involves researchers and students from the Schools of Agriculture and Food Sustainability, Maths and Physics, and Electronic Engineering and Computer Science as well as from QAAFI (Queensland Alliance for Agriculture and Food Innovation – co-funded by the Queensland Government). AAGI is a five-year strategic partnership across UQ, Curtin University, and the University of Adelaide that is aimed at harnessing analytics to drive the sector’s profitability and global impact.  

AAGI at UQ is funding PhD scholarships for analytics within the Grains Research and Development (GRDC) research portfolios of:
* Growing markets
* Crop nutrition and soil/water productivity
* Crop/cultivar development, breeding, phenology
* Farming systems integration
* Enabling cross-portfolio research
 

Research environment

These positions will be based at the St Lucia campus of UQ.
 

Scholarship

This project is supported by the Research project scholarship.

This scholarship includes:

  • living stipend of $36,400 per annum tax free (2025 rate), indexed annually
  • tuition fees covered.

This scholarship includes:

  • living stipend of $36,400 per annum tax free (2025 rate), indexed annually
  • tuition fees covered.
  • single overseas student health cover (OSHC)

Learn more about the Research project scholarship.

Supervisor

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 remote sensing data analysis and hyperspectral imaging would be of benefit to someone working on this project.

You will demonstrate academic achievement in the fields of applied mathematics, statistics, or data science and the potential for scholastic success.

A background or knowledge of crop phenotyping, AI model development, or feature engineering is highly desirable.

How to apply

You must submit an expression of interest (EOI) by 27 November, 2025 27 November, 2025.

Before you apply

  1. Check your eligibility for the Doctor of Philosophy (PhD).
  2. Prepare your documentation.
  3. If you have any questions about whether the project is suitable for your research interests, contact Dr Xin Guo (xin.guo@uq.edu.au).

When you apply

To apply, submit an expression of interest (EOI) for the program. You don't need to apply separately for the project or scholarship. How to submit an EOI

In your EOI, complete the ‘Scholarship/Sponsorship’ section with the following details:

  1. Are you applying for an advertised project: 'Yes'
  2. Project: 'Research project scholarship'
  3. Scholarship Code Listed in the Advertisement: PARAMETRISING-GUO
  4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/parametrising-ai-architecture-enhanced-canopy-trait-prediction-wheat-utilising-high-temporal-spatial-and-spectral-hypercubes

Submit an EOI