The AICF Institute AgriTwins program will be offering PhD/Masters of Philosophy scholarships for outstanding research scholars commencing in 2025.
Projects can be expanded below to detail the specific information on each digital twin, scholarship, ideal candidate criteria and eligibility requirements.
Expressions of Interest are to be submitted initially using the online form. Shortlisted candidates will be contacted through May and June 2025.
To be eligible for a scholarship, applicants must be domestic students as per the Higher Education Support Act at the time of award.
Domestic students include:
Project 1: PhD Project: Development of A Digital Twin of a Rice Handling and Processing Facility
This project aims to create a comprehensive digital twin of the Sunrice rice handling and processing facility to enhance operational efficiency, optimise resource utilisation, and improve product quality and sustainability. By leveraging historical data from the facility, the project will develop machine learning algorithms to build a digital twin, enabling detailed analysis, visualisation, and monitoring of rice processing steps.
Preferred candidate (skill set):
The preferred candidate should have strong programming and data science skills. Knowledge of digital twins and IoT will be desirable, and the application will be in agriculture.
Supervisory team:
Dr. Ibrar Yaqoob
Dr. Abdullah Abdullar
Dr. Jian Liu
Project 10: PhD Project: Integrated Australian Shorn Wool Production Forecasting System Using Digital Twin Technology
This innovative PhD project will develop a data-driven digital platform to support the forecast Australia’s shorn wool production. By integrating geospatial data (pasture growth, soil moisture, climate records) with industry data (livestock slaughter figures, wool test and wool auction volumes), the project aims to build a robust system that separates the impact of seasonal variation from changes in flock size and per head production. The system will provide quantitative forecasts of shorn wool production, but also insights into the quality profile of the Australian wool clip, helping producers, processors, and buyers make more informed decisions.
Key Research Activities:
Preferred candidate (skill set):
Applicants with backgrounds in agricultural science, remote sensing, environmental science, data analytics, economics, or systems modelling are encouraged to apply. Prior experience with coding (e.g. Python, R), GIS, or statistical modelling will be highly regarded.
Supervisor Team:
Dr. Ivan Maksymov (Primary supervisor),
Prof. Ganna Pogrebna,
Dr. Sue Hatcher,
Mr Jonathan Medway.
Project 11: PhD Project: Artificial Intelligence-Powered Climate Resilience Soil Model for Australian Broadacre Cropping and Livestock Producers
The Cool Soil Initiative (CSI) has already collected paddock-level data across 190 farms over a period of up to 5 years, including farm practices such as pulse crops, pasture rotation, fertilisation application, soil health parameters, and economic data. This project will integrate these datasets into a digital twin prototype model using the historic data from the current Global Smart Farm Network. This model will be enabled to undertake relational analysis of datasets to understand current climate-smart agricultural innovations and the drivers of resilience in the major Australian cropping region to:
* Increase the efficiency of farm inputs.
* Maximise system benefits and yield.
* Mitigate risks in extreme seasons.
Preferred Candidate skills:
A background in computer science/engineering or a demonstrated research record in AI/ML is highly desirable. Knowledge of soil and environmental science, digital twins, GIS, remote sensing, and IoT is advantageous.
Supervisor Team:
Dr. Ibrar Yaqoob (Primary Supervisor)
Dr. Abdullah (Co-Supervisor)
Matthew Muller (Industry Consultant)
Project 12: PhD Project: Using Data-Centric Tools to Develop a Comprehensive Digital Twin for Charles Sturt University’s Commercial Farm
The CSU Global Digital Farm is a 2,500-hectare farming enterprise consisting of two properties in Wagga Wagga and Orange, supporting a diverse range of agricultural production, teaching, and research activities. This project will investigate the spatial and temporal relationships among various soil, plant, and weather data, as well as crop management practices within the dryland cropping program. A range of existing crop and plant growth models (APSIM, Yield Prophet, etc.) that utilise these data and relationships will then be integrated to create a spatially enabled, digital twin-based decision support system focused initially on fertiliser management of cereals. With other management issues, crop types, and pastures also potentially able to utilise a similarly structured capability, an emphasis on developing a template-based framework will be employed to streamline future applications.
Preferred candidate (skill set):
The preferred candidate should have strong programming and data science skills. Knowledge of agricultural/environmental science, digital twins, GIS, remote sensing, and IoT will be desirable, and the application will be in the agricultural environment.
Supervisory team:
Professor Ganna Pogrebna
Professor Geoff Gurr
Mr. Jonathan Medway etc.
Project 15: PhD Project: Developing Computer Vision AI Models for use in Livestock Monitoring
This project aims utilise quantitative biometric and animal data into practical tools to assist livestock producers in detecting, monitoring, and rapidly intervening rapidly to improve and enhance animal productivity and welfare in feedlots and yards, utilising vision AI and data science techniques. The project will utilise animal data to develop vision transfer-based AI models to identify key features that have the most significant influence on animal condition and welfare metrics, such as BRD, pink eye, lameness, aggressive interactions, injuries, stress, movement, weight change, and shy feeding, among others. Subsequently, it will develop vision AI models of feedlot livestock that can simulate the impacts of livestock management interventions on animal welfare and production outcomes to inform the cost-benefit analysis of intervention strategies.
Preferred candidate (skill set):
The preferred candidate should have a background in computer science or engineering, or good knowledge of machine learning methods and the Python programming language. An MPhil, Master’s by research, or Honours degree is essential or a demonstrated research record. Knowledge of animal science, digital twins, IoT will be advantageous.
Supervisory team:
Dr. Mohammad Ali Moni
To be eligible to receive the scholarship applicants must:
Domestic students include:
* Australian citizens
* Australian permanent residents
* a person entitled to stay in Australia, or to enter and stay in Australia, without any limitation as to time
* a New Zealand citizen.
The scholarship provides the following benefits:
Stipend: This scholarship is valued at $42,483 per annum, payable in fortnightly instalments.
Tuition Fees: Domestic candidates: Fee exemption for a period equivalent to:
* 3.5 years (seven sessions) for PhD at full-time study.
* 2 years (4 sessions) for Masters by Philosophy at full-time study
Operating Funds: Scholarship candidates are allocated an allowance to assist with the reimbursement of costs associated with a candidate’s research.
PhD candidates
* Training - $5,000 pa for 3 years
* Travel - $5,000 in total
* Thesis Allowance - $840 in total- 3.5 years (seven sessions)
Masters by Research
* Training - $5,000 pa for 2 years
* Travel - $5,000 in total
* Thesis Allowance - $420 in total
The scholarship is tenable for:
PhD candidates - 3.5 years (3.5 years FTE for the stipend and tuition fee coverage, and 3 years for operating funds allowances) subject to satisfactory progress.
MPhil candidates - 2 years FTE for the stipend, tuition fee coverage, and operating funds allowances) subject to satisfactory progress.
Scholarship candidates are entitled to 20 paid annual leave working days per year and 10 paid sick leave days per year; however, they are not eligible for paid primary parental care leave or additional personal leave.
A two-stage process will be undertaken. Initial Expressions of Interest where candidates will respond to a specific project “pitch”.
Shortlisted candidates will be invited to apply for enrolment.
Selected candidates will then apply for an AgriTwins Scholarship when completing their Charles Sturt course admission application. When given the option to apply for a scholarship, select 'Yes', 'Full-time AGRTP', and 'Other'. Under 'Other', enter AgriTwins Scholarship Project.
Limited projects are now available. Shortlisting will conclude when all projects have been allocated.
Email aicf@csu.edu.au with the headline ‘AgriTwins student enquiries + project number’
