Industrial Transformation Training Centres - Grant ID: IC180100030
Funder
Australian Research Council
Funding Amount
$3,925,357.00
Summary
ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching ....ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching objectives are to enable development and adoption of new practices to improve productivity and asset reliability for industry and to foster a new maintenance technology service sector for national and international markets.Read moreRead less
Building insights of our largest terrestrial carbon sink: rangelands soils. Rangelands soils represent Australia’s largest carbon sink. Yet, little is known about their potential for carbon sequestration or their vulnerability to climate and environmental change. This project leverages investments in national terrestrial observation platforms and integrates previous research outputs to develop new methods to measure and build understanding of soil carbon composition and dynamics in rangeland eco ....Building insights of our largest terrestrial carbon sink: rangelands soils. Rangelands soils represent Australia’s largest carbon sink. Yet, little is known about their potential for carbon sequestration or their vulnerability to climate and environmental change. This project leverages investments in national terrestrial observation platforms and integrates previous research outputs to develop new methods to measure and build understanding of soil carbon composition and dynamics in rangeland ecosystems. Under a framework that connects detailed measurements and small-scale processes, with machine-learning, data-model assimilation and large-scale next-generation biogeochemical modelling, it’ll allow more accurate predictions of soil carbon change and better decision-making to guide sustainable rangelands management.Read moreRead less