Centre for Crop Science - Winter Research Programs 2026 Projects - Queensland Alliance for Agriculture and Food Innovation

General information on the program, including how to apply, is available from the UQ Student Employability Centre’s program website.

Compositional data modelling for biomass predictive machine learning

Primary Supervisors:

Dr Bruno Moreira and | b.moreira@uq.edu.au
A/Prof Sudhir Yadav | sudhir.yadav@uq.edu.au

Please contact Dr Burno (b.moreira@uq.edu.au) and A/Prof. Sudhir Yadav (sudhir.yadav@uq.edu.au) before applying.

Duration: 30 hours/week; onsite

This project looks into how to make better predictions for renewable energy materials by using data about the composition of biomass. We look into why parts that always add up to 100% can confuse machine learning models. We also try out log-ratio transformations and other methods to make messy compositional data into reliable inputs for predicting fuel properties.

Expected outcomes: Students are expected to gain skills in systematic literature data synthesis and practical machine learning workflows for compositional data. They may be required to generate a presentation upon project completion.

Suitability: Students with a background in agricultural, environmental, materials, chemical engineering, or computer science who are interested in sustainability and data analytics are encouraged to apply.

Accelerating Pigeonpea Improvement: Developing a Next-Generation Crossing Program for Short-Duration, Photoperiod-Insensitive Varieties in Australia

Primary Supervisor: Dr Mahen Sabampillai | m.sabampillai@uq.edu.au

Please contact Dr Mahen Sabampillai (m.sabampillai@uq.edu.au) before applying.

Duration: 30 hours/week; onsite

Location: UQ Gatton Campus

Pigeonpea (Cajanus cajan) is emerging as a high-value legume crop with strong potential to diversify Australian farming systems, improve soil health, and support climate-resilient agriculture. QAAFI/UQ has recently achieved a major milestone by developing and advancing the first new pigeonpea varieties toward commercialisation and Plant Breeders’ Rights (PBR) in Australia. To sustain this momentum, a structured and innovative crossing program is required to generate the next pipeline of elite genotypes with improved adaptation, yield stability, and seed quality.

Expected outcomes: The student will be involved in glasshouse tasks including plant tagging, phenotyping, recording flowering time and morphological traits, and assisting with seed handling and population advancement. They will learn best practices in experimental design, data collection, and digital record-keeping aligned with breeding pipeline standards. Basic exposure to molecular marker concepts and industry-focused breeding objectives will also be provided.

Suitability: Suitable for Graduate or Undergraduate Students.

Variation in physiological traits in a wheat breeding program

Primary Supervisor:

Karine Chenu | karine.chenu@uq.edu.au
Dr Alan Severini | a.severini@uq.edu.au

Please contact Karine Chenu (karine.chenu@uq.edu.au) and Dr Alan Severini (karine.chenu@uq.edu.au) before applying.

Duration: 20 - 36 hours/week; onsite

Location: UQ Gatton Campus

The research project involves assessing and evaluating contrasting genotypes from a wheat breeding program in a field experiment at Gatton. Important traits related to crop phenology, canopy development, radiation use efficiency and stay-green will be phenotyped to characterize genotypic differences underlying productivity. Those data will be used at a later stage to parameterise a crop model and for predictive purposes.

Expected outcomes: The scholar will gain skills in field trial measurements, crop physiology, data collection and analysis.

Suitability: Suitable for Graduate or Undergraduate Students with a background in biology/agronomy.