UniSQ 2025 PhD Degree in Machine Vision Technologies for Controlled Environment Agriculture

This project will advance automated technologies for machine vision sensing of plant health in Controlled Environment Agriculture (CEA), for example in vertical farming systems. CEA is projected to be of critical importance to meet food production targets on Earth to address finite land availability and a growing global population. Plant growth monitoring with machine vision is a key component of automating plant care in CEA, to complement human visual assessments of plant health and provide input for adaptive closed-loop control of plant water and nutrients.

• Table of Content
• About University of Southern Queensland
• PhD Degree in Machine Vision Technologies for Controlled Environment Agriculture
• Aim and Benefits of PhD Degree in Machine Vision Technologies for Controlled Environment Agriculture
• Requirements for PhD Degree in Machine Vision Technologies for Controlled Environment Agriculture Qualification
• Interview date, Process and Venue for PhD Degree in Machine Vision Technologies for Controlled Environment Agriculture
• Application Deadline
• How to Apply

PhD Degree in Machine Vision Technologies for Controlled Environment Agriculture

Aim and Benefits of PhD Degree in Machine Vision Technologies for Controlled Environment Agriculture

• Stipend of AUD $37,000
• Maximum period of tenure of an award is 3 years full-time.

Requirements for PhD Degree in Machine Vision Technologies for Controlled Environment Agriculture Qualification

To be eligible applicants must:
• Have completed an Honours Degree with First Class or 2A Honours, or equivalent level, or a Master’s degree with a significant research component;  
• Have applied, or currently enrolled in the PhD program at UniSQ;
• Not be receiving equivalent support providing a benefit greater than 75% of the student’s stipend rate;
• Be an Australian citizen or permanent resident;
• Be eligible to commence a PhD program at UniSQ Toowoomba in early 2025.

Interview date, Process and Venue for PhD Degree in Machine Vision Technologies for Controlled Environment Agriculture

To be eligible applicants must:   
• Have studied in robotics-related fields such as mechatronic engineering, electrical and electronics engineering, or computer science.
• Have experience in programming languages (e.g. Python) and machine vision and signal processing algorithms.
• Practical experience in electronic instrumentation and experimental trials is desirable.
• Ability to establish rapport with multidisciplinary industry representatives, for example related to Controlled Environment Agriculture, is desirable.

Check also:
Erasmus Mundus Scholarship 2027
Global Korea Scholarship 2027

Application Deadline

How to Apply

To apply, please ensure you have digital copies of the below information:

• Curriculum vitae; encompassing any research presentations and/or publications
• Education qualifications (testamur and academic transcripts) for all undergraduate and postgraduate awards,

Please directly contact Associate Professor Cheryl McCarthy by emailing [email protected], who will advise you of the application steps.  

For more details, visit UniSQ Scholarship webpage