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Field of Research : Autonomous agents and multiagent systems
Australian State/Territory : VIC
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  • Active Funded Activity

    ARC Future Fellowships - Grant ID: FT220100650

    Funder
    Australian Research Council
    Funding Amount
    $879,586.00
    Summary
    Improving the performance of Australian social insurance schemes. Applying methods from computational social science, this project aims to develop a novel, multi-level modeling framework to assist transport injury, workplace injury and disability insurance schemes consistently achieve and maintain standards of high performance as recognised by international benchmarks. By creating a virtual laboratory for policy-makers and scheme managers, it expects to generate a comprehensive understanding of .... Improving the performance of Australian social insurance schemes. Applying methods from computational social science, this project aims to develop a novel, multi-level modeling framework to assist transport injury, workplace injury and disability insurance schemes consistently achieve and maintain standards of high performance as recognised by international benchmarks. By creating a virtual laboratory for policy-makers and scheme managers, it expects to generate a comprehensive understanding of mechanisms driving insurance scheme performance, enabling comparison of anticipated outcomes in response to legislative changes, policy changes and management decisions. The project aims to help schemes avoid human and financial failure, benefitting people with injuries and disabilities while reducing scheme costs.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240100753

    Funder
    Australian Research Council
    Funding Amount
    $529,615.00
    Summary
    Autonomous Discovery of Green Inhibitors. The project aims to develop autonomous material design by integrating evolutionary algorithms and robotic experimentation. The project expects to pioneer a new method of materials discovery that could cut discovery times to 20% of traditional methods. Its expected to have significance through its discovery of new classes of corrosion inhibitors that are safe to both humans and the environment. The expected outcomes of this project will be a rapid disc .... Autonomous Discovery of Green Inhibitors. The project aims to develop autonomous material design by integrating evolutionary algorithms and robotic experimentation. The project expects to pioneer a new method of materials discovery that could cut discovery times to 20% of traditional methods. Its expected to have significance through its discovery of new classes of corrosion inhibitors that are safe to both humans and the environment. The expected outcomes of this project will be a rapid discovery methodology that can be used across materials science and new classes of safe corrosion inhibitors. This should provide significant benefits to workplace n safety and the environmental impact of the coatings industry while also increasing the rapid of innovation of new materials.
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