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Quantum-AI for Food Security and Student Wellbeing: Enhancing Hydroponic Resilience and Therapeutic Learning in Food Ladder’s Urban Ecosystems

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.
Value
• 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.
 

Students must be eligible under the universities PhD admission criteria in addition to meeting the eligibility requirements of the CSIRO Industry PhD Program. The university undertakes student eligibility checking against university PhD admission criteria.

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. 

 

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.

 
To apply, please ensure you have digital copies of the below information:
• Curriculum vitae; encompassing any research presentations and/or publications
• Preliminary Research Thesis Topic Proposal (DOTX 153KB), to be completed with Rajib Rana (Rajib.Rana@unisq.edu.au). 
• Education qualifications (testamur and academic transcripts for all undergraduate and postgraduate awards, 
Applicants must have their current/prospective supervisor complete the Supervisor support letter.
Application must be made via the UniSQ Scholarship Application Management System by the closing date.
If you require assistance in completing your application please download the Scholarships Online Application Manual (PDF 2.14MB).   
 
Position to remain open until filled.
For further information before applying, please contact Rajib Rana (Rajib.Rana@unisq.edu.au).