Scholarships
As of 2025, the Australian Institute for Machine Learning (AIML) has awarded over A$1 million in scholarship funding.
We are pleased to offer funding and scholarship opportunities for prospective students. Please see below for a current list of scholarships.
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Analytics for the Australian Grains Industry (AAGI) PhD Scholarships
Analytics for the Australian Grains Industry (AAGI) PhD Scholarships
The Analytics for the Australian Grains Industry (AAGI) initiative is a five-year strategic partnership to enhance the profitability and global competitiveness of the Australian grains sector through advanced analytics. This five-year initiative (2023-2027) is mainly funded by the Grains Research and Development Corporation (GRDC) with a $36 million investment and builds on the previous Statistics for the Australian Grains Industry 3 (SAGI3). Investment. The University of ºÚÁÏÉçÇø, in collaboration with Curtin University and The University of Queensland, is leveraging machine learning, data fusion, and statistics to support grain growers in making data-driven decisions
How to apply
For more information, including eligibility and application details, please refer to:
Open until filled.
List of projects and supervisors
Dr Matthew Knowling - Unifying on-farm data and crop models to enhance tactical crop decisions
Dr Mario Fruzangohar - Efficient construction and visualization of pangenomes for crops with large genomes
Professor Javen Shi - Improving genomic prediction accuracy using causal machine learning approaches
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CommBank Centre for Foundational AI PhD Scholarship
CommBank Centre for Foundational AI PhD Scholarship
This opportunity will support a PhD candidate to develop a research project aligned with the Centre’s core themes to build trustworthy and bias-aware AI systems, advance responsible reasoning and forecasting in deep learning, or to develop scalable, resource-efficient models for large-scale AI applications. The scholarship is valued at $40,000 AUD per annum and offers the opportunity to study at world-class research environment at AIML, collaborating with academic leaders and industry experts in foundational AI research.
How to apply
Please send a copy of your CV and academic transcript to Jessica Cortazzo, Manager, Project and Strategic Partnerships jessica.cortazzo@adelaide.edu.au
Open until filled.
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National Industry PhD Program Scholarship
National Industry PhD Program Scholarship
The is an Australian Government initiative to enhance workforce mobility among graduate researchers, and to promote knowledge transfer between academia and industries across all areas. PhD candidates under this program are connected with academic supervisors and industry-based researchers, to co-design innovative, applied research projects. Through their doctoral candidature, students will experience research in both university and industry settings, and undertake specialised training in research translation and commercialisation.
The University of ºÚÁÏÉçÇø and Lockheed Martin Australia invite applications for a project under this program, exploring the development of Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather prediction, signal tracking, fluid dynamics, and space exploration.
How to apply
Please visit the official scholarship page here for more details and to apply:
Project details
Title Advancing Signal Modelling with Physics-Informed Neural Networks Project description This project aims to develop Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather prediction, signal tracking, fluid dynamics, and space exploration. By incorporating physical laws into neural networks, we seek to improve accuracy, reduce data requirements, and lower costs for large-scale modelling tasks. PINNs enhance predictive capabilities and efficiency by combining data-driven methods with physical principles. Unlike traditional finite element methods, which demand extensive data and are costly, PINNs embed governing physical laws directly into the learning process. This allows effective management of limited and noisy data, resulting in faster and more cost-efficient signal modelling. However, embedding physical constraints within the network architecture remains a significant challenge. Current research often integrates these constraints into the training objective, complicating model training. This project aims to leverage advancements in computer vision, particularly in implicit neural representations, to embed priors in neural models through specific activation functions.
This project will be undertaken in collaboration with Dr Hemanth Saratchandran and Prof Simon Lucey of the Australian Institute for Machine Learning, and Advanced Systems & Technologies, Lockheed Martin Australia. The student will be co-located at the Australian Institute for Machine Learning at the University of ºÚÁÏÉçÇø and at Lockheed Martin offices located in ºÚÁÏÉçÇø and Melbourne.
Project sponsor University of ºÚÁÏÉçÇø and Lockheed Martin Australia – Advanced Systems & Technology Scholarship value $52,268 AUD -
Lockheed Martin Australia – Advanced Systems & Technology (LMA–AST) Scholarship
Lockheed Martin Australia – Advanced Systems & Technology (LMA–AST) Scholarship
This opportunity supports an exceptional student to develop novel deep learning algorithms and multi-object tracking methods for autonomous flight systems.
How to apply
Please send a copy of your CV and academic transcript to Jessica Cortazzo, Manager, Project and Strategic Partnerships jessica.cortazzo@adelaide.edu.au
Open until filled.
Project details
Title Vision-Based Sense and Avoid: Monocular Airborne Object Tracking for Safe Drone Flight Project description Join us on an exciting research project tackling the challenge of detecting and tracking tiny airborne objects using only a single camera onboard a drone. You will work with a massive, real-world dataset (5.9M+ images) covering diverse flight conditions, where objects can appear as just a few pixels in each frame. The goal is to develop novel deep learning algorithms and multi-object tracking methods that balance early detection with an extremely low false alarm rate—ensuring safe collision avoidance without unnecessary manoeuvres. By focusing on cutting-edge computer vision strategies (e.g., specialised small-object detection, temporal tracking, motion-based filtering), you will gain hands-on experience in one of the most critical problems for future autonomous flight systems. Check out for more background information on the dataset and benchmarks. Project sponsor Lockheed Martin Australia – Advanced Systems & Technology Group Eligibility - Be an Australian Citizen or permanent resident of Australia.
- Be enrolled, or plan on enrolling, in the following programs at the University of ºÚÁÏÉçÇø:
Bachelor of Computer Science (Honours) – refer to Bachelor of Computer Science (Honours) | Degree Finder for more information.
Masters of Artificial Intelligence and Machine Learning, Master of Data Science, Master of Computer Science, or Masters of Mathematical Sciences.
Scholarship value $5,000 AUD Supervisor Dr Feras Dayoub
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CSIRO Next Generation Graduates Program (NGGP) Scholarship
CSIRO Next Generation Graduates Program (NGGP) Scholarship
The CSIRO NGGP Scholarship has been established to support exceptional students engaged in advanced research in computer vision. The scholarship is valued at $15,000 payable in two equal instalments after each census date in March and August.
How to apply
Assessment of successful candidates will be made by a selection panel consisting of up to four AIML academics representing the University of South Australia, the University of ºÚÁÏÉçÇø, and the Defence Science and Technology Group (DSTG).
Applicants must be Australian citizens enrolled on a full-time basis in an approved honours program. Relevant checks (National Police Check, Australian Government security clearance) will be required. Scholarship holders must complete a 6-day work placement. Acceptance of the scholarship cannot be deferred.
This project was made possible by CSIRO’s Next Generation Graduates Program, an initiative that provides funding and support to attract and train the next generation of technology specialists.
List of projects and supervisors
Dr Feras Dayoub - Continual learning for mobile robot visual memory
- Continual learning and adaptation of resilient vision models in uncertain real-world environments
Dr Ravi Garg - Robust localisation and tracking with implicit and semi-explicit maps
- Self-supervised learning of single and multi-view splat
Dr Savitha Sam Abraham - Deep dive into SARfish datasets
Dr Josh Chopin (University of South Australia) - Evaluate the effectiveness of various adversarial machine learning techniques on Synthetic Aperture Radar (SAR) imagery
Associate Professor Belinda Chiera (University of South Australia) - Responsible AI, bias-aware machine learning, and data visualisation
Dr Lui Cirocco (University of South Australia) - BNN methods to investigate classification uncertainty qualification in identifying objects in a Navy UAV video dataset of maritime objects
Adjunct Associate Professor Ehsan Abbasnejad (Monash University)
Co-supervised by Dr Quoc Viet Vo
- Develop models that can incrementally learn about new data without forgetting previously acquired knowledge, a common issue known as catastrophic forgetting
- Development and application of continual learning algorithms that enable machine learning models to adapt and improve continuously in dynamic environments
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AIML Research Scholarships
AIML Research Scholarships
The AIML Research Scholarships support exceptional students engaged in advanced research in AI and machine learning. These scholarships are available for students undertaking their honours and postgraduate by coursework degrees.
For more information on the next round of scholarships, including eligibility and application details, please refer to:
Applicants must be citizens or permanent residents of Australia, citizens of New Zealand, permanent humanitarian visa holders or international students.
Applicants are invited to propose a research project and AIML supervisor of their choice, or select from this list of supervisors and available projects:
List of projects and supervisors
Dr Dhani Dharmaprani - Development of novel calibration methods for cardiac digital twins
- Correlation between invasive and non-invasive cardiac mapping
Dr Anh-Dzung Doan and Professor Tat-Jun Chin - Time domain adaptation in visual perception using Grand Theft Auto V data
Dr Feras Dayoub - Object detection for mobile robots
- Autonomous inventory management using robotic systems
Associate Professor Lingqiao Liu - Leveraging AI for educational material generation: transforming documents into slides with large language models
- Optimising 3D bin packing with machine learning (sponsored by industry partner Gale Pacific Ltd)
Dr Marcus Martens - Analysis of simulated spacecraft fragmentation using neuromorphic vision
Professor Minh Hoai Nguyen - 360Gaze: revolutionising eye tracking with immersive vision
- Action Recognition in Volleyball Using Computer Vision and Human Pose Estimation
- Help the helper: Predicting patients leaving a hospital bed in the near future for timely and smartly nurse intervention.
Dr Wei Zhang - Constructing knowledge graphs from clinical guidelines
Dr Henry Li - Generative machine learning models for accelerating CO2-capture material discovery
Dr Jinan Zou - Large language model (LLM) for AI education
Dr Xinyu Wang - Exploring the social attributes of large language models
Dr Qi Chen - Advance multimodal large language models' capacity of understanding, reasoning, and generation
Dr Zhibin Liao - Automated detection and tracking of marker particles in videos for diagnosing cystic fibrosis
Dr Xinyu Zhang - Aligning human intents efficiently in text-to-image generative models
Hear from our students
Joytu Khisha – AIML Research Scholarship Recipient
Joytu undertook his master's studies with the Embodied AI and Robotic Vision Research Group led by Dr Feras Dayoub. In this interview, Joytu shares his innovative research in mimicking of human movements and how this project has enabled him to fulfil his career aspirations.
Sarah Dickinson – Industrial AI Scholarship Recipient
Sarah’s research interests are in space exploration, stemming from her honours research in machine learning using techniques that measure gravitational waves. At AIML, she is supervised by Professor Tat-Jun Chin and the AI for Space Group to analyse lunar craters using satellite position tracking and computer vision technologies.
Oliver’s research will be examining anthropomorphism— the attribution of human qualities in objects—and how humans perceive consciousness when interacting with AI that possesses human-like features. His project is a joint collaboration between AIML and the University of ºÚÁÏÉçÇø’s School of Psychology, supervised by Professor Carolyn Semmler, Professor Anton van den Hengel, Dr Jon Opie, and Dr William Ngiam.
Ethan’s research focus is on monocular event-only Visual Odometry (VO)—a process that determines the position and orientation of an object, such as a camera or a robot —and Simultaneous Localisation and Mapping (SLAM), a computational method for developing digital maps, in order to create new applications for space operations. Ethan is also supervised by Professor Tat-Jun Chin.
Other scholarships
The University of ºÚÁÏÉçÇø offers a range of scholarships to undergraduate and postgraduate students. These scholarships, as well as many others funded by industry and non-profit organisations, are available to potential and currently enrolled students.
University Scholarships
The University offers a wide range of PhD scholarships for incoming students. A full list for scholarships is available here.