Grants & Opportunities

A Bayesian Approach to Distributed Estimation for Multi-Object Systems. This project aims to develop new signal processing techniques that facilitate autonomous technologies for environmental perception, with the ability to efficiently process large data volumes from multiple sensing modalities. Rapid advances in sensors and networks have led to a digital data deluge, from which extracting useful information presents new technological challenges and opportunities. To address this development, this project seeks to develop new distributed solutions for statistical estimation, which are specifically designed for dynamic systems with multiple object states, and are inherently scalable and robust. The potential benefits include new technologies for smart cities, autonomous infrastructure, and digital productivity.. Scheme: ARC Future Fellowships. Field: 0906 - Electrical and Electronic Engineering. Lead: Prof Ba Tuong Vo

Improving outcomes for young people transitioning from out-of-home care. The central aim of this project is to generate the new knowledge needed to support the development, implementation, and diffusion of evidence-based innovations for young people as they transition from out-of-home care to adulthood. The project is significant because young people living in out-of-home care are more likely to enter juvenile justice, become a teenage parent, be socially excluded, have mental and physical health problems and addictions. Outcomes include a world first longitudinal data evidence base, exemplars of best practice, and guidance to advance the application of transition pathways and plans to inform future innovations in Victoria and across Australia for improving transition from care with, by, and for young people. . Scheme: Linkage Projects. Field: 1117 - Public Health and Health Services. Lead: Prof Helen Skouteris

Understanding marine migratory connectivity for more sustainable oceans. Ocean basin-scale migrations of iconic sea turtles, marine mammals, seabirds, and fish expose them to multiple stressors and governance regimes, leading to gaps in management and population declines. The project aims to deliver the methods and evidence base of cross-taxa migratory connectivity that is essential to support the conservation of these species. Expected outcomes include comprehensive and integrated models of migratory connectivity, conservation theory development, and new methods that allow incorporation of migratory connectivity in conservation planning. Benefits include: a cross-taxa baseline that will enable Australia to measure environmental change in marine migratory connectivity for the first time.. Scheme: Discovery Projects. Field: 3103 - Ecology. Lead: A/Prof Daniel Dunn

Successful recipients of 2026 Business Growth Fund Round 7. Grant funding commitments at the time of announcement – 12 June 2026.

This grant opportunity aims to establish and deliver a National Digital Information Hub (Digital Hub) under the Commonwe...

In the 2024-25 Budget, the Australian Government provided funding to support the digital ability and literacy of First N...

In the 2024-25 Budget, the Australian Government provided funding to support the digital ability and literacy of First N...

Funding to support eligible small early childhood education and care providers to provide data in support of the Service...

AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing novel deep learning algorithms. The new algorithms will benefit a wide range of applications, e.g. to effectively use car crash training samples for accurately identifying potential road crashes in transport and to effectively use rare medical imaging training data for robustly diagnosing diseases in health.. Scheme: ARC Future Fellowships. Field: 0801 - Artificial Intelligence and Image Processing. Lead: Prof Wanli Ouyang

Advances in data integration modelling for infectious disease response. This project aims to develop powerful mathematical frameworks that integrate data from multiple sources to facilitate informed decisions in response to the threat of present, and future, infectious diseases. The project expects to generate new knowledge in mathematics by advancing the tools for incorporating multiple data sources into models of infectious diseases. The expected outcomes include enhanced capacity to predict spatiotemporal changes in transmission of infectious diseases. This project should provide significant benefits in the advancement of modelling techniques broadly applicable to infectious disease settings, which will be demonstrated for antimalarial drug resistance – a major threat to malaria elimination. . Scheme: ARC Future Fellowships. Field: 0102 - Applied Mathematics. Lead: Prof Jennifer Flegg

Inequality, Prosperity and the Australian Welfare State. This project aims to clarify contested understandings of Australian inequality and the role of economic and social policies in addressing policy challenges going forward. The objective of the project is to generate significantly improved knowledge of inequality in Australia using innovative approaches of data splicing, decomposition, simulation and backcasting to fill research gaps and resolve contested interpretations. We aim to provide a benchmark and robust framework against which policy development after the current crisis can be evaluated. This project aims to provide significant benefits, keeping Australia at the forefront of research on inequality and public policy, strengthening links between researchers and policy makers. . Scheme: Discovery Projects. Field: 1605 - Policy and Administration. Lead: Prof Peter Whiteford

A systems materials engineering strategy for hybrid ion capacitors. This project aims to develop a data science-driven approach to allow the use of materials systems engineering strategy to quantify the cell-level design of electrochemical energy storage devices such as hybrid ion capacitors. The intended outcomes of this project include new dynamic equivalent circuit models and a new quantitative approach to make the electrodes pairing predictable and realise their optimal design against the needs of the specific applications. It will also demonstrate a combined strategy of data science and discipline-specific experiments and theories to advance the emerging field of materials systems engineering. . Scheme: Discovery Projects. Field: 0912 - Materials Engineering. Lead: Prof Dan Li

Micro-electrofluidic platforms for monitoring 3D human biological models. The ability to study living cells and human biological models (cell cultures) delivers greater understanding of basic biological function and response to applied (bio)chemical stimuli. Creating the physical environments to sustain biological models, and mimic natural conditions and fluidic pathways, is immensely challenging, yet essential to deliver meaningful observational data. This project will deliver this capability through the convergence of expertise and innovation in analytical chemistry, materials science and cellular biology, ultilising the latest technology and understanding of 3D micro/electrofluidics, to enable the study and stimulation of advanced biological models, sustained within precisely controlled 3D micro-environments.. Scheme: Discovery Projects. Field: 0903 - Biomedical Engineering. Lead: Prof Brett Paull

Is there a climatic tipping point for Antarctic Bottom Water formation? Antarctic Bottom Water plays an important role in global ocean circulation and climate and yet its formation is also highly sensitive to climate change. This project will analyse new seafloor, core and water samples from the understudied Cape Darnley, East Antarctica, collected on a voyage in early 2022. This new data will be used in combination with an improved high resolution regional ocean model, to understand modern and past Antarctic Bottom Water formation under different climate states (warmer and colder than present), to determine if there are climate tipping points for the shut down of Antarctic Bottom Water formation. The anticipated benefits include a better understanding of future climate change on this important water mass.. Scheme: Discovery Projects. Field: 0405 - Oceanography. Lead: Prof Helen Bostock

Being a Transnational Muslim in Australia in an Era of Hyper-Security. Muslims have been the focus of significant policy articulations around security and integration in a hypersecuritised environment. This project aims to investigate how Australian Muslims are negotiating increased surveillance and public hostility and how this impacts on their sense of belonging. Working with members of four disparate Muslim communities in Brisbane, Melbourne, Perth and Sydney, the project will examine the varied manifestations of national and transnational belonging for conceptions of identity and social inclusion. In addition to generating new knowledge in the sociology of religion and migration studies, this project will also yield novel data for better policy and practice both locally and internationally.. Scheme: Discovery Projects. Field: 1608 - Sociology. Lead: Prof Adam Possamai

Governance for Gender Inclusion: Levelling the Field in Australian Sport. This project aims to understand why, despite gains in women's sport participation, gender inclusion efforts in Australian sport have not yet led to gender parity in leadership roles or broad accessibility for marginalised groups. It seeks to generate new knowledge about the regulatory mechanisms and social conditions that facilitate change through the development of a new interdisciplinary conceptual framework. Expected outcomes include enhanced analytic guidelines and robust recommendations for governance strategies, which can be applied to study other domains. This should provide significant theoretical and policy benefits by supporting equity in professional settings and health promotion through wider inclusion.. Scheme: Discovery Projects. Field: 1504 - Commercial Services. Lead: Prof Kathryn Henne

Dynamics of Suppressed Mixing Regimes in Australian Rivers. This study aims to further the fundamental science of turbulent mixing in the context of flow in Australian rivers. The focus is on prolonged low flow conditions which when coupled with warm surface temperatures cause the water column to become thermally stratified which then suppresses turbulent mixing. The extreme scale of the river systems has made investigating the true dynamics of the strongly stratified mixing regimes particularly challenging. By taking world first in-situ measurements of turbulent mixing and undertaking high resolution numerical simulations this study will provide definitive data which will allow correct characterization of the mixing regimes and how they are associated with river flow conditions.. Scheme: Discovery Projects. Field: 0915 - Interdisciplinary Engineering. Lead: Prof Steven Armfield

Scalable and Lightweight On-Device Recommender Systems. This project aims to address the resource-intensive and non-resilient nature of existing cloud-based personalised recommendation services. This project expects to generate new knowledge in the intersection of on-device machine learning and recommender systems. The expected outcomes include a novel auto-deployment platform that can efficiently customise a model for each user device's configuration, supporting on-device recommendation and model updates with tiny computational footprints. The benefits of these outcomes will position Australia at the forefront of AI and give numerous businesses the tools needed to deploy innovative business systems with a secure and cost-effective advantage.. Scheme: Discovery Early Career Researcher Award. Field: 4605 - Data Management and Data Science. Lead: Dr Tong Chen

The prevalence and impact of digital alcohol exposure. This project aims to use artificial intelligence to quantify the amount of alcohol people are exposed to in digital media (e.g., social media, streaming videos) in their daily lives and the effect alcohol exposure has on alcohol use. Expected outcomes for this project include a quantification of the amount of alcohol exposure in digital media and the impact it has on drinking and a development of a protocol to test exposure. Significant benefits are expected for policy makers aiming to reduce exposure and the public wanting to avoid exposure to limit the harm of alcohol. . Scheme: Discovery Early Career Researcher Award. Field: 5201 - Applied and Developmental Psychology. Lead: Dr Benjamin Riordan

Provides training to upskill leaders and workers in the construction and advanced manufacturing sectors with in-demand digital skills.

Partnership Projects will support collaborations, within the Australian context, that translate research evidence into h...

These invitation-only programs provide grant funding to innovative NGOs, think tanks, and national and international institutions to address root causes of systemic sustainability challenges in natural resource governance, environmental resilience, and education equity.

This program supports initiatives that build and disseminate best practice tools, research, and training to establish a strength-based policy framework for Indigenous-led nation building.

The BHP Foundation provides grant funding to innovative NGOs, think tanks, and national and international institutions to address systemic sustainability challenges in areas like natural resource governance, environmental resilience, education equity, and Indigenous peoples' self-determination. Applications are by invitation only.

The 2026-27 Budget is officially available at budget.gov.au as the authoritative source of Budget Papers (BPs) and Portfolio Budget Statement (PBS) documents. The 2026-27 Budget was tabled in the Parliament on Tuesday, 12 May 2026 (Budget night). This dataset is a collection of data sources from the 2026-27 Budget, including: * PBS Excel spreadsheets (including Table 2.X.1 Budgeted Expenses for Outcome X in machine readable format) – available after PBSs are tabled in the Senate (~8.15pm Budget night) * Selected tables from Budget Paper No. 1 will be available * Selected tables from Budget Paper No. 4 will be available (~8.15pm after Budget night) The data has been provided to assist those who wish to analyse and visualise key elements of the 2026-27 Budget. Data users should refer to footnotes and memoranda in the original files as these are not usually captured in machine readable CSVs. Information about the PBS Excel files and CSV: The PBS Excel files published should