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Scheme : ARC Future Fellowships
Research Topic : Multimedia Programming
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  • Funded Activity

    ARC Future Fellowships - Grant ID: FT0991052

    Funder
    Australian Research Council
    Funding Amount
    $788,800.00
    Summary
    Advanced computer simulations of star cluster evolution. The topic of this fellowship is dynamical simulations of star clusters using high-end graphics cards originally developed for the computer gaming industry. The proposed fellowship will thereby enhance Australia's capacity in the exploitation of high-performance computing and will give it a strong position in the new field of graphics card based simulations. The computer cluster built during the fellowship will also be an outstanding enviro .... Advanced computer simulations of star cluster evolution. The topic of this fellowship is dynamical simulations of star clusters using high-end graphics cards originally developed for the computer gaming industry. The proposed fellowship will thereby enhance Australia's capacity in the exploitation of high-performance computing and will give it a strong position in the new field of graphics card based simulations. The computer cluster built during the fellowship will also be an outstanding environment for the training of students. In addition, while Australia has a strong tradition in star cluster astronomy, it has so far not captured a leadership role in the theoretical analysis. The four years of this fellowship will allow it to establish a world-leading position in star cluster simulations.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT230100426

    Funder
    Australian Research Council
    Funding Amount
    $969,241.00
    Summary
    Rethinking Topological Persistence. This project aims to address the lack of transferability and uncertainty-awareness in AI models. Despite their success, AI models are met with bias and uncertainty when deployed in the real world. As a result, they are rarely used in high-risk industries like cybersecurity or transport. This project expects to build uncertainty-awareness into models by teaching them to return UNKNOWN when they encounter a previously unseen thing, instead of misclassifying it. .... Rethinking Topological Persistence. This project aims to address the lack of transferability and uncertainty-awareness in AI models. Despite their success, AI models are met with bias and uncertainty when deployed in the real world. As a result, they are rarely used in high-risk industries like cybersecurity or transport. This project expects to build uncertainty-awareness into models by teaching them to return UNKNOWN when they encounter a previously unseen thing, instead of misclassifying it. Further, the evaluation methods to be developed will not rely on access to test data, allowing cost-effective, private, and safe AI for high-stakes decision support. The outcomes will benefit Australia by accelerating economic investment and fostering greater social acceptance of AI.
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