Fully Funded PhD in Deep Learning and Drone Imaging for High-Throughput Cereal Phenotyping University of Melbourne in Canada

PhD opportunity at the University of Melbourne in Deep Learning and Drone Imaging for High-Throughput Cereal Phenotyping.

This project is hosted by CropGEM and focuses on developing next-generation AI and drone/UAV imaging methods for barley breeding and crop improvement. The successful candidate will work on automated detection, segmentation, and counting of barley heads from high-resolution drone imagery collected across large commercial breeding trials.

The research combines computer science, agriculture, machine learning, computer vision, spatial statistics, quantitative genetics, and phenomics. The project includes an existing annotated dataset of more than 2,000 RGB drone images and will expand to field sites across Victoria, South Australia, and Western Australia.

You will work with A/Prof Mohammad Pourkheirandish, Dr Patricia Menéndez, and Dr Robert Turnbull, with industry collaboration through InterGrain. The role offers experience in Python-based image analytics, large-scale data analysis, machine learning workflows, and industry-focused research translation.

Funding: fully funded PhD with stipend. The advertisement also points to University of Melbourne graduate research scholarships for benefit details.

Eligibility: applicants should have a relevant degree with a substantial research component and strong academic performance, or equivalent professional experience. Preferred backgrounds include machine learning, computer vision, statistical modelling, data analysis, HPC/cloud computing, agricultural science, or plant biology.

Deadline: 30 June 2026.

How to apply: email the required documents to A/Prof Mohammad Pourkheirandish, including a one-page suitability statement, a one- to two-page response to the selection criteria, CV, academic transcripts, and supporting materials.