{"meta":{"requested-page-number":1,"requested-page-size":20,"actual-page-size":20,"total-pages":1734,"total-size":34674,"search-description":null,"sort-by":""},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants?page%5Bnumber%5D=1&page%5Bsize%5D=20","first":"http://dataportal.arc.gov.au/RGS/API/grants?page%5Bnumber%5D=1&page%5Bsize%5D=20","last":"http://dataportal.arc.gov.au/RGS/API/grants?page%5Bnumber%5D=1734&page%5Bsize%5D=20","prev":"http://dataportal.arc.gov.au/RGS/API/grants?page%5Bnumber%5D=1&page%5Bsize%5D=20","next":"http://dataportal.arc.gov.au/RGS/API/grants?page%5Bnumber%5D=2&page%5Bsize%5D=20"},"data":[{"type":"grants","id":"LP250100426","attributes":{"code":"LP250100426","program-name":"Linkage","scheme-name":"Linkage Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LP  ","program":"Linkage","submission-year":2025,"round-number":1,"scheme-round":"LP25 Round 1"},"grantee":"University of Technology Sydney","grant-funder":"Australian Research Council","grant-title":"New fuels for proton-boron fusion: Towards clean energy production","grant-summary":"Laser-driven proton-boron fusion offers a radiation-free and sustainable nuclear energy source, but is currently constrained by the lack of effective fuels. In collaboration with HB11 Energy, this project aims to develop novel boron-based fuels with optimised composition and physical properties, leveraging our expertise in boron chemistry. We expect to advance knowledge in chemical synthesis, materials fabrication, laser engineering and nuclear physics. Achieving breakthroughs in this area could deliver significant economic and environmental benefits by enabling efficient fusion reactions that produce clean energy with high output.","lead-investigator":"Prof Zhenguo Huang","grant-value":546721.00,"grant-status":"Active","primary-field-of-research":"4016 - Materials Engineering","anticipated-end-date":"2028-12-31","investigators":"Prof Zhenguo Huang; Dr Limei Yang; Dr Sergey Pikuz","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LP250100426"}},{"type":"grants","id":"LE260100170","attributes":{"code":"LE260100170","program-name":"Linkage","scheme-name":"Linkage Infrastructure, Equipment and Facilities","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LE  ","program":"Linkage","submission-year":2025,"round-number":1,"scheme-round":"LE26 Round 1"},"grantee":"University of Technology Sydney","grant-funder":"Australian Research Council","grant-title":"Active Load-Pull Facility for Device, Circuit and System Characterisation ","grant-summary":"This project will establish advanced characterisation capabilities for the semiconductor industries by acquiring a state-of-the-art load-pull system. The system will support cutting-edge technologies, particularly in wireless integrated circuit design, enabling local businesses and research institutions to conduct essential development in Australia. By reducing reliance on overseas facilities, this facility will foster innovation, enhance economic growth, and support workforce development. It will help maintain Australia's competitive edge globally, create high-tech jobs, and encourage public-private collaborations. Expected outcomes include new intellectual property, stronger industry engagement, and enhanced research capabilities.","lead-investigator":"A/Prof Xi (Forest) Zhu","grant-value":720568.00,"grant-status":"Active","primary-field-of-research":"4009 - Electronics, Sensors and Digital Hardware","anticipated-end-date":"2026-12-31","investigators":"A/Prof Xi (Forest) Zhu; Prof Withawat Withayachumnankul; Prof Yonghui Li; Dr Maja Cassidy; A/Prof Jarryd Pla; A/Prof Dushmantha Thalakotuna; A/Prof Cuong Ton-That; Prof Karu Esselle; A/Prof Ke Wang; A/Prof Negin Shariati; Prof Michael Boers; Dr Bryan Schwitter; Dr Venkata Gutta","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LE260100170"}},{"type":"grants","id":"LE260100163","attributes":{"code":"LE260100163","program-name":"Linkage","scheme-name":"Linkage Infrastructure, Equipment and Facilities","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LE  ","program":"Linkage","submission-year":2025,"round-number":1,"scheme-round":"LE26 Round 1"},"grantee":"The University of New South Wales","grant-funder":"Australian Research Council","grant-title":"Facility for Integrated Sensing and Communication: Gigahertz to Terahertz","grant-summary":"This project aims to establish an ultrahigh bandwidth and resolution integrated sensing and communication facility in NSW, a key enabler for next generation of communication networks. Built on a photonic-based platform, this facility will significantly enhance resilience of Australia’s capabilities in telecommunications, healthcare and defence sectors. The expected outcomes will foster ground-breaking research in the combined area of millimetre-wave, terahertz and photonics for realising net zero emissions target of 6G networks. Benefits include the provision of critical emerging technologies for AI, computing and communications to significantly enhance Australia’s national interest in high-speed networks and smart sensing.","lead-investigator":"A/Prof Shaghik Atakaramians","grant-value":1409000.00,"grant-status":"Active","primary-field-of-research":"4006 - Communications Engineering","anticipated-end-date":"2026-12-31","investigators":"A/Prof Shaghik Atakaramians; Dr Deepak Mishra; Prof Jinhong Yuan; Prof Aruna Seneviratne; A/Prof Derrick Wing Kwan Ng; Dr Moritz Merklein; Prof Yang Yang; Prof Andrew Zhang; Dr Daniel Headland; Prof Benjamin Eggleton; Prof Sharath Sriram; Dr Rajour Tanyi Ako; Mrs Parisa Yadranjee Aghdam","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LE260100163"}},{"type":"grants","id":"LP250100069","attributes":{"code":"LP250100069","program-name":"Linkage","scheme-name":"Linkage Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LP  ","program":"Linkage","submission-year":2025,"round-number":1,"scheme-round":"LP25 Round 1"},"grantee":"The University of Melbourne","grant-funder":"Australian Research Council","grant-title":"Solar Facade Optimization Using Machine Learning and a Trustworthy Workflow","grant-summary":"This project aims to develop a solar façade design optimisation tool to support the uptake of building-integrated photovoltaic as a construction material. Most solar technologies are limited to roofs and current design tools fail to efficiently combine building performance simulations with design optimisation. This study enables the integration of solar technologies into building façades to enhance energy efficiency, reduce emissions and improve aesthetics. This will overcome complex trade-offs affecting multiple stakeholders in the supply chain, providing a new approach that balances energy production, costs, safety, and performance. It will yield economic benefits, support sustainability goals, and aid Australia’s transition to net zero.","lead-investigator":"Prof Rebecca Yang","grant-value":286662.00,"grant-status":"Active","primary-field-of-research":"4005 - Civil Engineering","anticipated-end-date":"2028-12-31","investigators":"Prof Rebecca Yang; Prof Lu Aye; Dr Feng Liu; A/Prof Xingliang Yuan; Dr Dingwen Bao; Prof Kamal Alameh; Mr Douglas Sum; Mr Daniel Moore; Mr Robert Buck","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LP250100069"}},{"type":"grants","id":"LP250201093","attributes":{"code":"LP250201093","program-name":"Linkage","scheme-name":"Linkage Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LP  ","program":"Linkage","submission-year":2025,"round-number":2,"scheme-round":"LP25 Round 2"},"grantee":"The University of Adelaide","grant-funder":"Australian Research Council","grant-title":"A Self-Sufficient Ecosystem for Resilient Rural Communities","grant-summary":"Many rural Australian communities lack reliable infrastructure to support modern technologies due to limited grid access and communication constraints. This project will develop a modular, AI-enabled cyber-physical system powered by solar microgrids to build self-sufficient ecosystems in off-grid rural areas. By integrating distributed sensing, communication networks, and smart analytics, the system will support essential services such as rural agriculture, water purification, and connectivity. The project will produce a scalable and adaptable solution tailored to off-grid and resource-constrained environments. It will benefit long-term sustainability, productivity, and quality of life in Australia’s rural and remote communities.","lead-investigator":"Prof Peng Shi","grant-value":496224.00,"grant-status":"Active","primary-field-of-research":"4606 - Distributed Computing and Systems Software","anticipated-end-date":"2028-12-31","investigators":"Prof Peng Shi; A/Prof Wei Zhang; Dr Xin Yuan","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LP250201093"}},{"type":"grants","id":"LP250200996","attributes":{"code":"LP250200996","program-name":"Linkage","scheme-name":"Linkage Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LP  ","program":"Linkage","submission-year":2025,"round-number":2,"scheme-round":"LP25 Round 2"},"grantee":"University of Wollongong","grant-funder":"Australian Research Council","grant-title":"Cryptographically Secure Code: Detection and Generation","grant-summary":"Cryptographic algorithms in software, especially those produced by modern AI code generators, often contain security vulnerabilities. This project aims to investigate efficient and reliable techniques for enhancing the security of code using innovative automated detection and generation techniques. This project expects to generate new knowledge on methods for detecting and mitigating cryptographic vulnerabilities in code. Expected outcomes include the development of effective technologies for producing highly secure software. The resulting technologies will support Australia’s digital innovation, enhance cybersecurity resilience, and deliver long-term economic and societal benefits by improving software security and productivity.","lead-investigator":"Prof Willy Susilo","grant-value":537909.00,"grant-status":"Active","primary-field-of-research":"4604 - Cybersecurity and Privacy","anticipated-end-date":"2028-12-31","investigators":"Prof Willy Susilo; Dr Siqi Ma; A/Prof Fuchun Guo; Prof Yang-Wai Chow; Prof Son Lam Phung; A/Prof Khoa Nguyen; Dr Fendy Santoso; Dr Yudi Zhang","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LP250200996"}},{"type":"grants","id":"LP250200754","attributes":{"code":"LP250200754","program-name":"Linkage","scheme-name":"Linkage Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LP  ","program":"Linkage","submission-year":2025,"round-number":2,"scheme-round":"LP25 Round 2"},"grantee":"University of Wollongong","grant-funder":"Australian Research Council","grant-title":"Adaptive AI-Driven Sustainable Home Energy Ecosystem","grant-summary":"This project will develop an adaptive artificial intelligence driven home energy system that integrates solar panels, stationary batteries, household appliances and electric vehicles as storage. Neural networks will forecast energy use, solar generation and battery condition, while reinforcement learning will schedule devices to balance cost, comfort and battery health. The system will be validated through simulation and hardware trials with industry input. Expected outcomes include reduced household energy bills, improved grid stability, longer battery life and enhanced industry capabilities. This research supports a cleaner energy future, delivers practical tools for users and strengthens collaboration between research and industry.","lead-investigator":"Prof Haiping Du","grant-value":437793.00,"grant-status":"Active","primary-field-of-research":"4007 - Control Engineering, Mechatronics and Robotics","anticipated-end-date":"2028-12-31","investigators":"Prof Haiping Du; Prof Lina Yao; Miss Yijun Yang; A/Prof Md. Rabiul Islam; Dr Yadan Luo","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LP250200754"}},{"type":"grants","id":"LP250200050","attributes":{"code":"LP250200050","program-name":"Linkage","scheme-name":"Linkage Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LP  ","program":"Linkage","submission-year":2025,"round-number":2,"scheme-round":"LP25 Round 2"},"grantee":"RMIT University","grant-funder":"Australian Research Council","grant-title":"3D composites for fire-proof EV charging facilities in bushfire-prone areas","grant-summary":"This project aims to develop fire-resistant and functional three-dimensional woven composites for protecting electric vehicle charging infrastructure in bushfire-prone regions by advancing material design, fire modelling and scalable manufacturing. It expects to generate new knowledge in fire-safe composite materials through innovative combinations of cost-effective flame retardants, structural optimisation and embedded sensing. Expected outcomes include composite systems tailored to bushfire exposure, manufacturing-ready prototypes and strengthened industry collaboration. This should provide significant benefits in supporting safe transport electrification, enhancing bushfire resilience and advancing Australia’s manufacturing capabilities.","lead-investigator":"Prof Thuy (Kate) Nguyen","grant-value":586100.00,"grant-status":"Active","primary-field-of-research":"4005 - Civil Engineering","anticipated-end-date":"2029-12-31","investigators":"A/Prof Hsu-Chiang Kuan; Prof Thuy (Kate) Nguyen; Prof Jun Ma; Mr Wes Reynolds","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LP250200050"}},{"type":"grants","id":"DP260104454","attributes":{"code":"DP260104454","program-name":"Discovery","scheme-name":"Discovery Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"DP  ","program":"Discovery","submission-year":2025,"round-number":1,"scheme-round":"DP26 Round 1"},"grantee":"The Australian National University","grant-funder":"Australian Research Council","grant-title":"Electrically Tuneable Nanowires for Infrared Hyperspectral Imaging","grant-summary":"This project aims to develop an innovative short to mid-wave infrared hyperspectral imaging system using electrically tuneable III-V nanowire photodetectors. By integrating nanomaterial synthesis, device fabrication, and computational reconstruction algorithms, the project will demonstrate high-performance, room-temperature photodetectors with broad, dynamically adjustable spectral responsivity as the core of a short to mid-wave infrared hyperspectral imaging system. The outcomes will address the limitations of the current technology, which is bulky and expensive, enabling transformative applications in environmental monitoring, gas sensing, industrial quality control, and biomedical diagnostics.","lead-investigator":"Prof Lan Fu","grant-value":702876.00,"grant-status":"Active","primary-field-of-research":"4009 - Electronics, Sensors and Digital Hardware","anticipated-end-date":"2028-12-31","investigators":"Prof Lan Fu; Dr Chaohao Chen; Dr Zhe Li","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/DP260104454"}},{"type":"grants","id":"DP260101084","attributes":{"code":"DP260101084","program-name":"Discovery","scheme-name":"Discovery Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"DP  ","program":"Discovery","submission-year":2025,"round-number":1,"scheme-round":"DP26 Round 1"},"grantee":"Swinburne University of Technology","grant-funder":"Australian Research Council","grant-title":"Integration Optimisation Algorithms of Fibre-reinforced Printing Structures","grant-summary":"This project aims to develop integration optimisation algorithms for designing lightweight, high-performance, and printable fibre-reinforced composite (FRC) structures, and create an advanced design tool for Australian academics and engineers. By simultaneously optimising structural topology and layer-by-layer printing paths limited by 3D printing process, the project generates lightweight FRC designs with superior performance and manufacturability. The project expects to advance knowledge in the field of computational optimisation, enabling the establishment of a world-leading fibre-reinforced 3D printing platform. This will also deliver significant economic and social benefits in reducing material and energy consumption, and emissions.","lead-investigator":"Prof Xiaodong Huang","grant-value":721334.00,"grant-status":"Active","primary-field-of-research":"4017 - Mechanical Engineering","anticipated-end-date":"2029-09-13","investigators":"Prof Xiaodong Huang; Prof Dong Ruan; Dr Shanqing Xu; Dr Weibai Li","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/DP260101084"}},{"type":"grants","id":"LP250100011","attributes":{"code":"LP250100011","program-name":"Linkage","scheme-name":"Linkage Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LP  ","program":"Linkage","submission-year":2025,"round-number":1,"scheme-round":"LP25 Round 1"},"grantee":"La Trobe University","grant-funder":"Australian Research Council","grant-title":"Fast Nondestructive Imaging Platform for Farm Produce Analysis","grant-summary":"This project aims to develop a next-generation fast and nondestructive farm produce analysis platform leveraging advanced hyperspectral imaging technology. This project expects to generate new knowledge in the area of computational imaging, mathematical modelling of the point spread function, super-resolution and deep internal learning. Expected outcomes include data-driven imaging methods for an integrated, more efficient analysis of the internal and external characteristics of farm produce. This should provide significant economic, commercial and social benefits, enabling smarter farming practices that can respond more effectively to modern agriculture challenges and help position Australia as a leader in future precision agriculture.","lead-investigator":"A/Prof Peng Cheng","grant-value":435426.00,"grant-status":"Active","primary-field-of-research":"4606 - Distributed Computing and Systems Software","anticipated-end-date":"2028-12-31","investigators":"A/Prof Peng Cheng; Dr Hui Cui; Prof Yi-Ping Phoebe Chen; Prof Tony Bacic; Prof Yonghui Li; Dr Ting Zhang; Dr Andrew Squires; Dr Changyang Li","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LP250100011"}},{"type":"grants","id":"LP250200991","attributes":{"code":"LP250200991","program-name":"Linkage","scheme-name":"Linkage Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LP  ","program":"Linkage","submission-year":2025,"round-number":2,"scheme-round":"LP25 Round 2"},"grantee":"La Trobe University","grant-funder":"Australian Research Council","grant-title":"Privacy-Aware Resource Optimisation for Distributed AI in SMEs","grant-summary":"This project aims to develop a secure and privacy-preserving AI system that enables many users to train large models without sharing their data.\nIt expects to generate new knowledge in managing delays and interruptions in distributed training and in designing intelligent scheduling tools for efficient use of shared computing resources. Expected outcomes include scalable training algorithms, smart resource schedulers, and deployable prototypes supporting privacy-preserving AI services. This should provide significant benefits by making advanced AI more accessible and trustworthy for small and medium businesses, while reinforcing Australia’s leadership in digital capability, cybersecurity, and data privacy.","lead-investigator":"Prof Wei Xiang","grant-value":429077.00,"grant-status":"Active","primary-field-of-research":"4606 - Distributed Computing and Systems Software","anticipated-end-date":"2028-12-31","investigators":"Prof Wei Xiang; Dr Di Wu; Prof Xiaohui Tao; Prof Jianming Yong; Dr Wencheng Yang; Mr Kai Zou","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LP250200991"}},{"type":"grants","id":"LP250200943","attributes":{"code":"LP250200943","program-name":"Linkage","scheme-name":"Linkage Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"LP  ","program":"Linkage","submission-year":2025,"round-number":2,"scheme-round":"LP25 Round 2"},"grantee":"La Trobe University","grant-funder":"Australian Research Council","grant-title":"Agentic Digital Twin for Sustainable AI Factory","grant-summary":"This project aims to reduce energy consumption and carbon footprint of AI infrastructure by developing an Agentic Digital Twin (ADT), which is an intelligent system powered by Large Language Models capable of real-time prediction, reasoning, and control of energy usage and heat flows. Co-designed with three industry partners representing both upstream AI infrastructure and downstream energy reuse sectors, including agriculture and hydrogen, the ADT is capable of cutting energy waste and enables circular heat reuse. Expected outcomes include reduced emissions, enhanced energy efficiency, and circular energy utilisation, contributing to Australia’s net-zero targets and strengthening national capabilities in sustainable, intelligent computing.","lead-investigator":"Prof Wei Xiang","grant-value":762177.00,"grant-status":"Active","primary-field-of-research":"4605 - Data Management and Data Science","anticipated-end-date":"2028-12-31","investigators":"Prof Wei Xiang; Dr Zhe Chen; Prof Yi-Ping Phoebe Chen; A/Prof Chang Xu; Dr Kan Yu; Mr Anthony Ling; Mrs Antonia Collings; Dr Tianlong Ni","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/LP250200943"}},{"type":"grants","id":"DP260101725","attributes":{"code":"DP260101725","program-name":"Discovery","scheme-name":"Discovery Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"DP  ","program":"Discovery","submission-year":2025,"round-number":1,"scheme-round":"DP26 Round 1"},"grantee":"University of South Australia","grant-funder":"Australian Research Council","grant-title":"Improving interfacial solar evaporation via lowering evaporation enthalpy","grant-summary":"This project aims to develop effective strategies to significantly improve the clean throughput via interfacial solar evaporation (ISE). This will be realized via design of functional materials that can lower water evaporation enthalpy (i.e., energy required for phase change of water from liquid to vapor), therefore improving the evaporation rate with the same energy input and material consumption. Expected outcomes include new knowledge in material-water molecule interactions, optimized ISE technology and improved clean water yield. This should significantly benefit the residents in remote communities who suffer from severe clean water shortages and position Australia as a global leader in sustainable and affordable desalination.","lead-investigator":"Prof Haolan Xu","grant-value":722169.00,"grant-status":"Active","primary-field-of-research":"4016 - Materials Engineering","anticipated-end-date":"2028-12-31","investigators":"Prof Haolan Xu; A/Prof Gary Owens; Dr XUAN WU","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/DP260101725"}},{"type":"grants","id":"DP260102225","attributes":{"code":"DP260102225","program-name":"Discovery","scheme-name":"Discovery Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"DP  ","program":"Discovery","submission-year":2025,"round-number":1,"scheme-round":"DP26 Round 1"},"grantee":"The University of New South Wales","grant-funder":"Australian Research Council","grant-title":"Biotransforming Food Waste into High-value Alcohols","grant-summary":"Transforming food waste into valuable products provides a great opportunity to tackle environmental issues and attain a circular economy. This project aims to develop an innovative technology and the underpinning science to gain renewable high-value alcohols from food waste directly on a low-carbon, economical and self-sustaining platform and realise sustainable food waste reduction. An increasing quantity of carbon-rich food waste is generated abundantly in Australia and worldwide, that typically represents a substantial, but largely untapped, renewable resource. The intended outcome of the project will shift food waste management from energy-consuming to energy-producing process and accelerate Australia’s transition to net-zero emissions.","lead-investigator":"Prof Bing-Jie Ni","grant-value":1049688.00,"grant-status":"Active","primary-field-of-research":"4011 - Environmental Engineering","anticipated-end-date":"2030-12-31","investigators":"Prof Bing-Jie Ni; Dr Wei Wei; Dr Haoran Duan; Dr Xuran Liu","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/DP260102225"}},{"type":"grants","id":"DP260101827","attributes":{"code":"DP260101827","program-name":"Discovery","scheme-name":"Discovery Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"DP  ","program":"Discovery","submission-year":2025,"round-number":1,"scheme-round":"DP26 Round 1"},"grantee":"The University of New South Wales","grant-funder":"Australian Research Council","grant-title":"Engineering Robust Anti-Icing Coatings for Extreme Environments","grant-summary":"This project is focused on the development of durable, anti-icing surfaces designed for extreme environments, combining ice-repellency and self-heating capabilities with exceptional hardness and toughness. This offers a novel solution for preventing condensation freezing and ice accumulation in cold, humid conditions. Anticipated\noutcomes include a coating with superior ice mitigation abilities and an unprecedented resistance to mechanical abrasion and erosion. Such advancements are expected to bring significant advantages to industries associated with aviation, air-conditioning, and renewable energy, where robust, anti-icing surfaces are essential for maintaining operational efficiency and extending service life.","lead-investigator":"Prof Paul Munroe","grant-value":847706.00,"grant-status":"Active","primary-field-of-research":"4016 - Materials Engineering","anticipated-end-date":"2028-12-31","investigators":"Dr Zhifeng Zhou; Prof Paul Munroe; Prof Zonghan Xie; Dr Yujie Chen","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/DP260101827"}},{"type":"grants","id":"DP260102294","attributes":{"code":"DP260102294","program-name":"Discovery","scheme-name":"Discovery Projects","funding-commencement-year":2026,"scheme-information":{"scheme-code":"DP  ","program":"Discovery","submission-year":2025,"round-number":1,"scheme-round":"DP26 Round 1"},"grantee":"The University of New South Wales","grant-funder":"Australian Research Council","grant-title":"Spin-Resolved Studies of Quantum Spin Liquids","grant-summary":"This project is dedicated to advancing the study and practical applications of quantum materials. It focuses on the exploration and understanding of the intriguing flatband and quantum spin liquid phases. The successful completion of this project is expected to yield ideal material platforms for in-depth studies of flatband and quantum spin liquid phenomena, as well as for potential future applications in quantum computing and spintronic devices. This endeavor holds the promise of significantly enhancing the research and industrial capabilities in the quantum computing and spintronic sectors within Australia.","lead-investigator":"Dr Zhi Li","grant-value":793336.00,"grant-status":"Active","primary-field-of-research":"5104 - Condensed Matter Physics","anticipated-end-date":"2029-12-31","investigators":"Dr Zhi Li; Prof Sven Rogge; Prof Chao Zhang; Prof Susan Coppersmith; Prof Cui-Zu Chang","grant-priorities":[]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/DP260102294"}},{"type":"grants","id":"ID260100164","attributes":{"code":"ID260100164","program-name":"National Intelligence Grants","scheme-name":"National Intelligence Discovery Grants (NIDG)","funding-commencement-year":2026,"scheme-information":{"scheme-code":"ID  ","program":"National Intelligence Grants","submission-year":2026,"round-number":1,"scheme-round":"ID26 Round 1"},"grantee":"The University of New South Wales","grant-funder":"Office of National Intelligence","grant-title":"Unlocking the potential of biopolymers for mechanical energy harvesting","grant-summary":"This project aims to optimize biopolymer mechanical-to-electrical energy conversion by engineering polymer structures to decouple dipole-ion mechanisms while developing measurement methods to isolate individual electro-mechanical contributions. The significance lies in creating energy harvesters with optimised electrical output, while being both conformable to biological tissue and biocompatible. This has the potential to power covert wearable devices indefinitely, eliminating batteries and enabling persistent intelligence-gathering and health monitoring through continuous power generation from breathing, walking, and physiological processes.","lead-investigator":"A/Prof Damia Mawad","grant-value":784901.00,"grant-status":"Active","primary-field-of-research":"3403 - Macromolecular and Materials Chemistry","anticipated-end-date":"2029-11-01","investigators":"A/Prof Damia Mawad; A/Prof John Daniels","grant-priorities":[{"priority-type":"The Intelligence Challenges","priority-name":"Emerging biological science challenges"},{"priority-type":"The Intelligence Challenges","priority-name":"Emerging material science challenges"}]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/ID260100164"}},{"type":"grants","id":"ID260100375","attributes":{"code":"ID260100375","program-name":"National Intelligence Grants","scheme-name":"National Intelligence Discovery Grants (NIDG)","funding-commencement-year":2026,"scheme-information":{"scheme-code":"ID  ","program":"National Intelligence Grants","submission-year":2026,"round-number":1,"scheme-round":"ID26 Round 1"},"grantee":"The Australian National University","grant-funder":"Office of National Intelligence","grant-title":"Biological Language Models in Threat Detection and Response","grant-summary":"This project proposes researching artificial intelligence tools in synthetic biology to jointly identify harmful biological sequences that are invisible to current detection methods, and rapidly generate prophylactics that can neutralise sudden or emerging biological hazards.\nAdvances in artificial intelligence are revolutionising how biology is studied and engineered. Generative AI can create new biological agents (such as proteins and viruses) that offer solutions to challenges in health and industry. However, the same technology can also be used maliciously to create novel and potent toxins and pathogens.\nThe project will deliver AI-enabled systems capable of detecting harmful artificial biological agents and designing rapid interventions to counteract them.\nThis work will address a critical vulnerability in Australian security by strengthening national capabilities in biological threat detection and response, ultimately protecting public health and the economy.","lead-investigator":"Dr Matthew Spence","grant-value":793320.00,"grant-status":"Active","primary-field-of-research":"3102 - Bioinformatics and Computational Biology","anticipated-end-date":"2029-08-13","investigators":"Dr Matthew Spence; Prof Eric Stone; Prof Colin Jackson","grant-priorities":[{"priority-type":"The Intelligence Challenges","priority-name":"Data-driven and real-time analytical challenges"},{"priority-type":"The Intelligence Challenges","priority-name":"Emerging biological science challenges"}]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/ID260100375"}},{"type":"grants","id":"ID260100366","attributes":{"code":"ID260100366","program-name":"National Intelligence Grants","scheme-name":"National Intelligence Discovery Grants (NIDG)","funding-commencement-year":2026,"scheme-information":{"scheme-code":"ID  ","program":"National Intelligence Grants","submission-year":2026,"round-number":1,"scheme-round":"ID26 Round 1"},"grantee":"Griffith University","grant-funder":"Office of National Intelligence","grant-title":"Understanding new threats of extremism in Australia: Causes, consequences and solutions","grant-summary":"This project examines the threat of sovereign citizen (SovCit) extremism in Australia—a movement identified by the Australian Security Intelligence Organisation as a growing national security concern. Despite increasing incidents of SovCit-linked violence, little empirical research exists into what drives this ideology or how to mitigate its risks. This project aims to: identify the prevalence of this ideology; distinguish the factors driving SovCit beliefs from those triggering violence; test a grievance-based theoretical framework; and inform intervention strategies for authorities. Using police bodyworn camera footage, a national survey, and targeted sampling and analysis of SovCit adherents, the project will produce new insights into the drivers of SovCit extremism and practical strategies to help authorities identify, manage, and mitigate threats. Outcomes will inform practical, evidence-based tools to protect public officials and improve responses to anti-government extremism.","lead-investigator":"Prof Kristina Murphy","grant-value":799801.00,"grant-status":"Active","primary-field-of-research":"4402 - Criminology","anticipated-end-date":"2029-12-30","investigators":"Prof Kristina Murphy; A/Prof Keiran Hardy","grant-priorities":[{"priority-type":"The Intelligence Challenges","priority-name":"Human behaviour and influence challenges"}]},"links":{"self":"http://dataportal.arc.gov.au/RGS/API/grants/ID260100366"}}]}