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 on a rolling basis from September to November with commencement by the end of 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: SunRice PhD Project 1 – Development of the PaddyVision® instrument for rapid and non-destructive rice quality analysis
SunRice have developed an instrument called the PaddyVision®, which is the first of its kind in the world. The PaddyVision® non-destructively measures paddy rice quality at the point where it is received from a farmer and is a global advance in the real-time analysis of the potential milling quality of rice. This project is an excellent opportunity to be at the forefront of deep learning and machine learning while developing PaddyVision® as a global standard in rice quality measurement. With latest image analysis methodology and ground truth data, this project will explore various machine learning models for predicting milling quality and implemented into PaddyVision®.
Preferred candidate (skill set):
A background in computer science/engineering or a demonstrated research record in AI/ML is highly desirable. Knowledge of agricultural/environmental science, digital twins, GIS, remote sensing, and IoT is advantageous.
Supervisory team:
Dr. Ibrar Yaqoob
Dr. Abdullah Abdullah
Dr. Jian Liu
Project 2: SunRice PhD Project 2 - Using Data Science and Digital Twin technology on Development of An Integrated Geospatial Rice Production Decision Support System
This project utilises publicly available information such as water allocations, dam inflows, historical rainfall, and crop competition, along with internal data including production volumes, yield, paddy prices, quality, consumer demand, sales, and milling capacity. Using this comprehensive data, the project develops advanced algorithms capable of generating predictive scenarios to help determine optimal commercial offerings, thereby maximising returns for growers and shareholders. Additionally, it creates sophisticated algorithms that segment and profile growers based on historical performance to forecast future outcomes. To enhance stakeholder engagement and decision-making, this project is also developing a digital twin system that provides easy visualisation and utilisation of these insights.
Preferred candidate (skill set):
A background in computer science/engineering or a demonstrated research record in AI/ML is highly desirable. Knowledge, experience, and passion for interdisciplinary research in agriculture, as well as digital twins, GIS and remote sensing are advantageous.
Project 3: PhD Project: Vineyard 3D Simulation Modelling for Vineyard Optimisation
This project explores advanced 3D simulation technology to enhance the management and productivity of the vineyard. The project aims to simulate various management scenarios and evaluate their impacts, by constructing a comprehensive virtual model that encompasses the vineyard’s topography, soil properties, plant variety, and environmental conditions. This model will enable functions to predict the outcomes of different irrigation schedules, nutrient applications, and pest control measures on vine growth, fruit yield, and overall vineyard health.
Preferred candidate (skill set):
A background in computer science or engineering or a proven research record in AI/ML is highly desirable. Knowledge, experience, and passion for interdisciplinary research in horticulture and environmental science, as well as digital twins and simulation models, are advantageous.
Project 9: PhD Project: Development of Digital Twin for Decision-Support Framework in Variable-Rate Application of Pre-Emergent Herbicides
This project is to build models on environmental and geospatial data to determine a decision support framework for variable rate application of pre-emergent herbicides. Using a digital twin virtual farming system aggregates field topography, soil parameters, previous operations and satellite imagery to create zones and scenarios that assess best economical and productive rate of herbicides, reducing crop injury and potentially increasing weed control efficacy. This approach determines the bio-economically optimum herbicide rate for specific zones within paddocks while adhering to registered label rates.
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.
Supervisory team:
Dr. Fendy Santoso
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),
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 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’
