Special Research Initiatives - Grant ID: SR0354908
Funder
Australian Research Council
Funding Amount
$10,000.00
Summary
The Insect-Plant Chemical Ecology Network (IPCEN). We bring together plant molecular biology, entomology and analytical chemistry to transform three leading fields of Australian research into an advanced science with far reaching capabilities in innovative research and applied outcomes. Expertise studying the biochemical pathways that produce specific plant compounds and expertise in insect recognition and response to these chemicals will be brought together. This will lead to new research outco ....The Insect-Plant Chemical Ecology Network (IPCEN). We bring together plant molecular biology, entomology and analytical chemistry to transform three leading fields of Australian research into an advanced science with far reaching capabilities in innovative research and applied outcomes. Expertise studying the biochemical pathways that produce specific plant compounds and expertise in insect recognition and response to these chemicals will be brought together. This will lead to new research outcomes and solutions to problems in agriculture, horticulture, forestry and protection of Australia's native flora. Researchers are struggling to create these links, constrained by disciplinary boundaries and geographical isolation. Key industries and researchers already support this proposal.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100090
Funder
Australian Research Council
Funding Amount
$250,000.00
Summary
Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object ....Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object recognition in images, speech recognition and automatic translation, bringing the prospect of machine intelligence closer than ever. Modern machine learning techniques have had huge impact in the last decade in fields such as robotics, computer vision and data analytics. The facility would enable Australian researchers to develop, learn and apply deep networks to problems of national importance in robotic vision and big data analytics. Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC190100017
Funder
Australian Research Council
Funding Amount
$3,703,664.00
Summary
ARC Training Centre for Integrated Operations for Complex Resources. This Training Centre aims to increase value in mining through clever applications of ‘lean processing’ and train the next generation of scientists and engineers in advanced sensors and data analytics in complex resources; knowledge priorities for the mining industry. Sensor information will be linked to the resource’s in-place knowledge to enable data analytics of all embedded knowledge. Processing can then be tuned to resource ....ARC Training Centre for Integrated Operations for Complex Resources. This Training Centre aims to increase value in mining through clever applications of ‘lean processing’ and train the next generation of scientists and engineers in advanced sensors and data analytics in complex resources; knowledge priorities for the mining industry. Sensor information will be linked to the resource’s in-place knowledge to enable data analytics of all embedded knowledge. Processing can then be tuned to resource attributes, maximising value ‘on the fly’. Benefits will include increasing certainty on product quality and maximising throughput and recovery. Outcomes will include new tools to rapidly model geological and geometallurgical uncertainty with sensor inputs, to track the resource to product and enhance interpretation.Read moreRead less