Improving plant phenotyping accuracy via spatio-temporal 3D data analysis

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
   

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

The research environment available to the project is world-class. UQ has a strong and internationally focused research culture. It is ranked in the top 1% of world universities in three widely publicized international University rankings.

The Data Science group researches and develops innovative and practical solutions for business, scientific, and social applications in the realm of big data. 

The group encompasses a variety of research strengths including data and knowledge engineering, information retrieval, computer vision, and complex and intelligent systems. 
 

This project is supported by the Research project scholarship.

Learn more about the Research project scholarship.

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 solid programming and algorithmic skills, knowledge in the area of machine learning and computer vision, particularly 3D vision, VLMs, and VLAs, would be of benefit to someone working on this project.

You will demonstrate academic achievement in the fields of computer vision and agriculture science and the potential for scholastic success.

A background or knowledge of linear algebra and optimization algorithms is highly desirable.

You must submit an expression of interest (EOI) by 30 September, 2026 30 September, 2026.

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 Associate Professor Mahsa Baktashmotlagh (m.baktashmotlagh@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: PHENOTYPING-BAKTASHMOTLAGH
4. Link to Scholarship Advertisement: https://study.uq.edu.au/study-options/phd-mphil-professional-doctorate/projects/improving-plant-phenotyping-accuracy-spatio-temporal-3d-data-analysis

Submit an EOI