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Field of Research : Machine learning not elsewhere classified
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Machine learning not elsewhere classified (7)
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  • Researchers (29)
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP230102934

    Funder
    Australian Research Council
    Funding Amount
    $449,148.00
    Summary
    The Material Science of Biomimetic Soft Network Composites. Nature combines stiff and strong collagen fibres intertwined within a weak polymer matrix of proteoglycans into soft tissues with outstanding mechanical durability and biological properties. We converge a biomimetic design strategy inspired in the architecture of natural soft tissues and a novel additive manufacturing technology termed melt electrowriting (MEW) to manufacture advanced biomimetic soft network composites (BSNC). The SNCs .... The Material Science of Biomimetic Soft Network Composites. Nature combines stiff and strong collagen fibres intertwined within a weak polymer matrix of proteoglycans into soft tissues with outstanding mechanical durability and biological properties. We converge a biomimetic design strategy inspired in the architecture of natural soft tissues and a novel additive manufacturing technology termed melt electrowriting (MEW) to manufacture advanced biomimetic soft network composites (BSNC). The SNCs are composed of a weak polymer matrix and a MEW reinforcing fibrous phase printed at the nanometre scale, containing patterns mimicking the natural tissue architectures. Advanced computational tools are applied for the rational design of the SNC while reducing costs and times associated to experimental work.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP230101439

    Funder
    Australian Research Council
    Funding Amount
    $390,000.00
    Summary
    Planet Formation at Solar System Scales with the James Webb Space Telescope. Planetary systems like our own form within vast disks of primordial gas and dust around newborn stars. This project will observe such disks spanning a range of ages with the James Webb Space Telescope to reveal the detailed in-situ physics of planet-forming disks themselves. We will deliver the sharpest-ever infrared images in astronomy, exploiting the only Australian-designed instrument on the spacecraft: the Aperture .... Planet Formation at Solar System Scales with the James Webb Space Telescope. Planetary systems like our own form within vast disks of primordial gas and dust around newborn stars. This project will observe such disks spanning a range of ages with the James Webb Space Telescope to reveal the detailed in-situ physics of planet-forming disks themselves. We will deliver the sharpest-ever infrared images in astronomy, exploiting the only Australian-designed instrument on the spacecraft: the Aperture Masking Interferometer. This yields new physics for actively growing protoplanets, carved rings and gaps in disks, and gravitationally sculpted patterns of leftover cometary debris. Confronting state-of-the-art models with these data will immediately yield profound insights into planetary system formation, including our own.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP230101122

    Funder
    Australian Research Council
    Funding Amount
    $420,000.00
    Summary
    Build competency aware and assuring machine learning systems. Recent development in machine learning (ML) has seen ML models with extremely high prediction accuracy. However, to support human-machine partnership in decision-making in complex environments, beyond accuracy, it is essential for ML systems to be competency aware and reliable, and at the same time be exploratory. This project aims to develop novel techniques to equip a ML system with the ability to identify own competency, to justify .... Build competency aware and assuring machine learning systems. Recent development in machine learning (ML) has seen ML models with extremely high prediction accuracy. However, to support human-machine partnership in decision-making in complex environments, beyond accuracy, it is essential for ML systems to be competency aware and reliable, and at the same time be exploratory. This project aims to develop novel techniques to equip a ML system with the ability to identify own competency, to justify its competency and decisions, to explore unknown situations and fully utilise existing expertise to deal with unknowns. The expected outcomes of the project will enable ML systems to become truely intelligent and reliable machine partners for human decision makers in a wide range of applications.
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    Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE240100816

    Funder
    Australian Research Council
    Funding Amount
    $444,000.00
    Summary
    Probing dark energy with the largest 3D Map of the Universe. Dark Energy is one of the most profound mysteries of modern physics. It makes up about 70 percent of the Universe, but no compelling theory can explain its nature. This project aims to measure the properties of Dark Energy with unprecedented accuracy: an order of magnitude better than the state of the art. It aims to accomplish this by extracting information from the largest 3D map of the cosmos, built with the optical spectra of 35 mi .... Probing dark energy with the largest 3D Map of the Universe. Dark Energy is one of the most profound mysteries of modern physics. It makes up about 70 percent of the Universe, but no compelling theory can explain its nature. This project aims to measure the properties of Dark Energy with unprecedented accuracy: an order of magnitude better than the state of the art. It aims to accomplish this by extracting information from the largest 3D map of the cosmos, built with the optical spectra of 35 million galaxies, observed by the Dark Energy Spectroscopic Instrument. This project will foster Australia's historic leadership and investments in galaxy surveys via unique international partnerships, and produce cutting-edge tools for big data analyses with important applications in a wide range of industries.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP230100301

    Funder
    Australian Research Council
    Funding Amount
    $616,000.00
    Summary
    Expanding the Foundation of Planetary Science. Our understanding of the Solar System is based on a foundation of meteorite analyses. Knowing their orbital origin provides a critical spatial context, but we have this data for <0.1% of samples. This project aims to address this issue. There are 66 meteorite falls across Australia with orbits determined by the Desert Fireball Network that await recovery - more than the current global dataset. This project expects to generate new knowledge by applyi .... Expanding the Foundation of Planetary Science. Our understanding of the Solar System is based on a foundation of meteorite analyses. Knowing their orbital origin provides a critical spatial context, but we have this data for <0.1% of samples. This project aims to address this issue. There are 66 meteorite falls across Australia with orbits determined by the Desert Fireball Network that await recovery - more than the current global dataset. This project expects to generate new knowledge by applying an innovative search methodology using drones and machine learning. Expected outcomes include dramatically increasing the number of orbital meteorites. This should provide significant benefits. By linking meteorites to their parent asteroids every rock becomes a small sample-return mission.
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    Active Funded Activity

    Towards A Green And Sustainable Energy-efficient Metaverse.

    Funder
    Australian Research Council
    Funding Amount
    $440,145.00
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    Active Funded Activity

    Discovery Projects - Grant ID: DP240102050

    Funder
    Australian Research Council
    Funding Amount
    $452,390.00
    Summary
    Data Complexity and Uncertainty-Resilient Deep Variational Learning. Enterprise data present increasingly significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions. They continue to significantly challenge the state-of-the-art probabilistic and neural learning systems with limited to insufficient capabilities and capacity. This research aims to develop a theory of flexible deep v .... Data Complexity and Uncertainty-Resilient Deep Variational Learning. Enterprise data present increasingly significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions. They continue to significantly challenge the state-of-the-art probabilistic and neural learning systems with limited to insufficient capabilities and capacity. This research aims to develop a theory of flexible deep variational learning transforming new deep probabilistic models with flexible variational neural mechanisms for analytically explainable, complexity-resilient analytics of real-life data. The outcomes are expected to fill important knowledge gaps and lift critical innovation competencies in wide domains.
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    Showing 1-7 of 7 Funded Activites

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