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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Responsible AI Research (RAIR) Centre PhD Scholarships
Responsible AI Research (RAIR) Centre Scholarships
The Responsible AI Research (RAIR) Centre PhD Scholarship opens doors to conducting research that will shape how AI impacts society. Based at the University of Adelaide, transitioning to Adelaide University from 1 January 2026, the RAIR Centre brings together the innovation power of the State Government of South Australia, the University of Adelaide, and CSIRO's Data61 in an unprecedented partnership across four research themes.
The scholarship is valued at $36,600 per annum.
How to apply
For more information, including eligibility and application details, please refer to:
Applicants must be an Australian citizen, a permanent resident of Australia or an international student who is able to meet the eligibility criteria to study for a PhD at the University of Adelaide in the stated field. Noting that for Theme 1, there is an additional requirement for applicants to hold an undergraduate and/or postgraduate degree in Mathematics.
**Please note, only the Theme 1 scholarship is still available, all the other Theme PhDs have been filled.**
Open until filled. Applications must be received between July–December 2025.
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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 Adelaide, 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
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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 Dr Kathy Nicholson (kathy.nicholson@adelaide.edu.au).
Open until filled.
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National Industry PhD Program Scholarships
National Industry PhD Program Scholarships
The National Industry PhD Program 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.
Project details
Title Run-time Monitoring of Machine Learning for Robotic and Autonomous Perception Project description This project aims to make robotic perception systems safer and more reliable by developing new techniques that continuously monitor the performance of their machine learning models (i.e. keeping an eye on the system’s "brain" to quickly spot when it starts to struggle or behave unexpectedly).
Our goal is to create tools that continuously evaluate the performance of these models and quickly detect any decline or unexpected behaviour. The system will be designed to recognise when it encounters unfamiliar conditions—whether these involve unusual appearances, shapes, or spatial relationships of objects—and to indicate when it is unsure about its input.
Key expected outcomes include the creation of monitoring algorithms that identify early signs of performance issues, and the development of predictive models to anticipate potential failures. Additionally, the project will facilitate the transfer of this technology to industry, while also advancing academic knowledge through publications and presentations at leading conferences.
This project will be undertaken in collaboration with Dr Feras Dayoub of the Australian Institute for Machine Learning, and Advanced Systems & Technologies, Lockheed Martin Australia. The student will co-located at the Australian Institute for Machine Learning at the University of Adelaide and at Lockheed Martin offices.
Project sponsor University of Adelaide and Lockheed Martin Australia – Advanced Systems & Technology Scholarship value $52,268 AUD annually Application portal
Apply now National Industry PhD Program Scholarships: CommBank Centre for Foundational AI Research
AIML at Adelaide University and the Commonwealth Bank of Australia (CommBank) invite applications for four industry PhD projects, advancing the understanding and practical capabilities of transformers for financial services.
Adelaide University and CommBank are pleased to offer four, fully funded PhD opportunities, for projects under the Australian Government’s National Industry PhD Program. Successful candidates for these projects will receive:
- Admission to a PhD program at Adelaide University;
- An Adelaide University Research Scholarship for 4 years, paid at $53,635 p.a., and a tuition fee waiver;
- Supervision from research specialists at Adelaide University and CommBank;
- Industry embedment with CommBank; and
- Access to professional development opportunities through the University’s Graduate Research and Innovation Training program, and the National Industry PhD Program’s research translation workshop series.
Successful candidates will be located at the CommBank Centre for Foundational AI Research at AIML, and will receive expert supervision from academics in the School of Computer Science and Information Technology, and research specialists at CommBank.
Further information about the four projects, and eligibility requirements, can be found via the links below. To apply, please follow the instructions in the advertisement, including providing all relevant documentation. If you would like to be considered for more than one of the opportunities, please submit a single application, indicating your preferred project(s).
Title Project #1: Data-Efficient Transformers: Effective Training from Small Datasets
Project description Transformers have revolutionised fields with access to large datasets. However, their performance degrades significantly in domains with limited data, creating a barrier to innovation in many scientific and industrial settings. This project will advance the understanding and practical capabilities of transformers trained on small datasets. Building on structured initialisation techniques that incorporate inductive bias, we will refine these methods and explore novel attention mechanisms that allow more flexible representations in low-data regimes.
Title Project description This PhD project will explore novel approaches to improving the parameter efficiency of large-scale transformer models, such as Vision Transformers (ViTs) and Large Language Models (LLMs), with a focus on reducing fine-tuning and training costs without sacrificing model performance. It will build upon recent advances in low-rank matrix adaptation techniques (e.g., LoRA) and extend preliminary work that introduces high-frequency sinusoidal augmentation to improve the expressive capacity of low-rank matrices.
Title Project #3: Transaction-Aware Transformers: Tailoring Deep Learning for Financial Modelling
Project description Transformers are a cornerstone of deep learning, succeeding in language, vision, and time-series modelling. While recent advances have tailored transformers to domains such as vision and language, financial applications remain underserved. This project will develop transaction-aware transformer architectures designed for financial sequences. We will explore strategies that capture the categorical and hierarchical nature of transactions, study how model depth influences learning dynamics, and design initialization schemes adapted to the statistics of financial data. By aligning architecture with the structure of transaction data, we aim to improve performance and interpretability in key financial tasks such as fraud detection, customer segmentation, and behavioural analysis.
Title Project description Homomorphic encryption (HE) lets computers work directly on encrypted data, so the data stays private even during processing. Transformers are today’s leading AI models for language and vision. Putting them together would allow financial institutions to run valuable analytics without ever exposing customer data, but standard transformers use operations that HE cannot do efficiently. This project redesigns the transformer from how it reads inputs to how it does its core calculations, so every step works with homomorphic encryption. The result is an AI system that makes accurate predictions while the data stays encrypted, protecting privacy, meeting compliance, and enabling safe collaboration with partners.
General enquiries
For further information about the National Industry PhD Program, or research degrees at Adelaide University, please contact the Adelaide University Graduate Research School.
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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 Hilary Brookes (hilary.brookes@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 The Airborne Object Tracking Challenge hosted by AIcrowd 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 Adelaide:
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 Adelaide, 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 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 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)
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
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Industrial AI Program Scholarships
Industrial AI Program Scholarships
Launched in 2024, AIML’s Industrial AI program supports the development of core capability in industrial AI, driving economic growth and job creation in South Australia (SA) and across the nation in a range of sectors. As part of the program, AIML is offering competitive scholarships for high achieving domestic and international students.
Australian Institute for Machine Learning (AIML) Industrial AI Program Supplementary Scholarship (MPhil)
Eligibility
Applicants must be citizens of Australia, the United Kingdom, or the United States of America, or Australian permanent residents who are acceptable as candidates for a Master of Philosophy (MPhil) degree (refer eligibility) at the University of Adelaide.
Stipend
The scholarship will be tenable for up to 2 years and has a stipend of $10,000 per annum.
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 Lack – Industrial AI Scholarship Recipient
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 Adelaide’s School of Psychology, supervised by Professor Carolyn Semmler, Professor Anton van den Hengel, Dr Jon Opie, and Dr William Ngiam.
Ethan Elms – Industrial AI Scholarship Recipient
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 Adelaide 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.