Designing Narrow Orchard Systems with Artificial Intelligence and Digital Twins

As the digital transformation of agriculture accelerates, the industry is in growing hunger for highly educated and multidisciplinary talents to fully unlock its vast potential. Particularly those from ICT backgrounds with essential AgTech R&D experiences are highly demanded. This PhD scholarship has been set up to identify and foster such a future leader by including her/him in the University of Queensland (UQ) and the Agriculture Victoria Research (AVR) joint adventure for innovation of Narrow Orchard Systems (NOS).

The NOS project will provide opportunities for the PhD student to deploy high-end hardware and software tools for creating 3D orchard digital twins, innovate artificial intelligence (AI) approaches to recognise fruit quality, implement high-performance simulations for orchard designs, and actively contribute to the development of a decision-support tool aimed at advancing next-generation planting systems.

The successful candidate will be enrolled at UQ (one of the world’s Top50 universities), study at QAAFI (Queensland Alliance for Agriculture & Food Innovation), spend half of her/his candidature in the AVR Tatura SmartFarm (a globally reputed leader in orchard and AgTech innovations), and have access to state-of-the-art resources in both institutions (including data and high-performance sensing and computing systems).

She/he will be supported by an advisory team across UQ and AVR, and work within a multi-disciplinary team of computer scientists, horticulturalists and AgTech specialists located in multiple states of Australia.

This project is supported by the Research project scholarship.

Learn more about the Research project scholarship.

You must contact the principal supervisor for this project to discuss your interest. You should only complete the online application after you have reached agreement on supervision.

Always make sure you are approaching your potential supervisor in a professional way. We have provided some guidelines for you on how to contact a supervisor.

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 machine learning and high-performance computing would be of benefit to someone working on this project.

You will demonstrate academic achievement in the field/s of computer programming and software development and the potential for scholastic success.

A background or knowledge of image processing and pattern recognition is highly desirable.

To be considered for this scholarship, please email the following documents to Dr Liqi Han (liqi.han@uq.edu.au):

• Cover letter
• CV
• Academic transcript/s
• Evidence for meeting UQ's English language proficiency requirements eg TOEFL, IELTS

Please note the following: Submitting the above documents does not constitute a full application for admission into The University of Queensland's PhD program. If you are selected as the preferred applicant, you will then be invited to submit a full application for admission. You can familiarise yourself with the documents required for this process on the UQ Study website.