Winter School in AI and Predictive Agriculture

The Winter School in AI and Predictive Agriculture is a free event to share the collective learnings of experts from The University of Queensland in the use of AI and predictive analytics in Agriculture and Sustainability. The event will be held on 13-15 July 2026 at The University of Queensland St Lucia Campus, Brisbane and will feature seminars, case studies and hands-on workshops.

The Winter School aims to:

• Build capacity in applied AI for agriculture, especially amongst PhD students, EMCRs and industry participants;
• Provide participants with practical skills, not just knowledge;
• Demonstrate AI as an enabler to make standard agricultural technologies more accessible.

A rough agenda is below, you can submit an expression of interest to attend one or more of the events:

Monday 13 July:

AI skills, foundational tools, and key concepts (neural networks, transformers, agents, and AI ecosystems).

All day: Lecture, online or in person.

Tuesday 14 July:

Phenotyping applications, multimodal data handling and example workflows.
AM: Lecture, online or in person.
PM: Hands-on workshop, in person only.

Wednesday 15 July:

Genomic prediction – genotype-to-phenotype modelling, G×E×M modelling, CGM-WGP, and digital twins.
AM: Lecture, online or in person.
PM: Hands-on workshop, in person only.

Spaces for hands-on workshops are strictly limited.

We are currently seeking expressions of interest for this event, please complete the EOI form here if you would like to participate >

Scientific and Technical Committee

David Kainer

Senior Research Fellow, The University of Queensland, ARC Centre of Excellence for Plant Success

Genomic prediction and graphs.

Alex Wu

Senior Research Fellow, Queensland Alliance for Agriculture and Food Innovation

Field-level design/prediction, optimisation and machine learning with crop models to explore G×E×M interactions and design optimal crop ideotypes.

Liqi Han

Research Fellow, Queensland Alliance for Agriculture and Food Innovation

Crop Digital Twins, High Performance Computing, Artificial Intelligence, High Throughput Phenotyping.

Brodie Lawson

Postdoctoral Researcher, Queensland University of Technology, ARC Centre of Excellence for Plant Success

Crop growth modelling across scales, including plant signalling, cell-growth and physiology. Gaussian process regression as a surrogate to true modelling to create fast models for crop simulators.

Christos Mitsanis

PhD Student, The University of Queensland, ARC Centre of Excellence for Plant Success

Agentic AI, mechanistic networks and CGMs.

Sophie (Qiaomin) Chen

Research Fellow, The University of Queensland

Multi-scale precision agriculture. Integrates crop modelling with machine learning. modelling of crop traits using simulation-generated data sets.

Sebastian Lopez-Marcano

Senior Manager Analytics, The University of Queensland

Data analytics, workflows.

Javier Fernandez

Research Fellow, The University of Queensland

Crop and agronomic modelling.

Eric Dinglasan

Research Fellow, Queensland Alliance for Agriculture and Food Innovation

Optimisation strategies for QTL stacking models. Adapts methods proven in animal breeding to crops.