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.

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

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 

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

 

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.

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

The 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.

 

Industrial AI Program Scholarships

We are pleased to offer Industrial AI Program Scholarships for students undertaking their honours or higher degree by research (HDR) studies in AI and machine learning. Funded by the state government, these scholarships are available for honours, master's and PhD students.

For more information on each of these scholarships, including eligibility and application details, refer to these pages:

Postgraduate scholarships

Applicants must be citizens of Australia, the United Kingdom, or the United States of America, or Australian permanent residents who are acceptable for candidacy for their relevant degree at the University of ºÚÁÏÉçÇø.

Industrial AI Program Scholarships at PhD level include travel funding to gain valuable experience working with international collaborators in the United Kingdom or the United States of America.

Industrial AI PhD Scholarship key points

 

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
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

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.

 

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 of ºÚÁÏÉçÇø

University Scholarships

The University offers a wide range of PhD scholarships for incoming students. A full list for scholarships is available here.