UniSQ 2025 Quantum-AI for Food Security and Student Wellbeing

This project develops quantum-AI tools to optimise urban hydroponic food systems and evaluate their therapeutic impact on student well-being. The expected outcome is to create quantum-reinforced AI algorithms for resource-efficient hydroponics and evidence-based frameworks for integrating green space exposure for improved student wellbeing. This project will potentially enhance urban food resilience, reduce resource waste, and create scalable mental health interventions, advancing national sustainability and education priorities.

Table of Content

• Summary
• Benefits
• Requirments
• Interview Process
• Application Deadline
• How To Apply

Summary

• Application DeadlineDecember 31, 2025
• Study LevelPhD
• SponsorUniversity of Southern Queensland
• Eligible CountryAustralia , New Zealand

Benefits

• Stipend of AUD $47,020
• Maximum period of tenure of an award is 4 years. Periods of study already undertaken towards the degree will be deducted from the period of tenure.

Requirements

To be eligible for a CSIRO Industry iPhD scholarship, students must:

• be an Australian or New Zealand citizen, or Australian permanent resident
• meet university PhD admission and English language requirements
• not have previously completed a PhD
• not be in receipt of another primary scholarship
• be able to commence the program in the year of the offer
• enrol as a full-time PhD student. The Program is not designed for part-time arrangements and are strongly discouraged. However, part-time may potentially be considered only if approved by the supervisory team and in accordance with university policy. Part-time scholarships are taxable.
• be located at the agreed project location(s) and, if required, comply with the university’s external enrolment procedures. 

Selection Process

Skillset: 

• Proficient in Python, machine learning, and preferably quantum machine learning.
• Strong interest in sustainability, education, and youth development.   

Key Responsibilities:

• Apply reinforcement learning to optimise resource allocation in hydroponic systems.
• Analyse mixed-methods data (quantitative biometrics + qualitative focus groups).
• Collaborate with educators, engineers, and industry partners.

Application Deadline

How To Apply

For more details, visit UniSQ Scholarship webpage