Discovery Early Career Researcher Award - Grant ID: DE180101138
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
A multi-scale risk assessment platform for inhaled carbon nanotubes. This project aims to develop a coherent risk assessment platform to evaluate human respiratory exposure to carbon nanotubes. Compared to the exponential growth of carbon nanotubes technology, capability of inhalation risk assessment is lagging. The project expects to generate new knowledge on the unique role and risk of carbon nanotube geometry. It will develop a new transport model and create a unified risk assessment. The exp ....A multi-scale risk assessment platform for inhaled carbon nanotubes. This project aims to develop a coherent risk assessment platform to evaluate human respiratory exposure to carbon nanotubes. Compared to the exponential growth of carbon nanotubes technology, capability of inhalation risk assessment is lagging. The project expects to generate new knowledge on the unique role and risk of carbon nanotube geometry. It will develop a new transport model and create a unified risk assessment. The expected outcome is the enhanced risk assessment capability of human exposure to carbon nanotubes, which will provide a significant benefit to the nanotechnology industry through ensuring safety in developing an emergent technology.Read moreRead less
Modelling network innovation performance capability: a multidisciplinary approach. Innovation is created in complex network interactions.
By combining agent-based and fuzzy logic modelling, this project will identify combinations of resources to generate new ideas/technologies. This will enable managers and policy makers to understand the mechanisms behind innovation and implement policies aimed at enhancing innovation processes.
Modelling human perceptual-motor interaction for human-machine applications. This project aims to develop a new modelling framework for identifying the perceptual-motor processes that underlie cooperative and competitive human interaction. The project will also determine whether this modelling framework can be combined with modern machine-learning methods to develop artificial agents capable of human level performance. Expected outcomes will include a practical methodology for rapidly generating ....Modelling human perceptual-motor interaction for human-machine applications. This project aims to develop a new modelling framework for identifying the perceptual-motor processes that underlie cooperative and competitive human interaction. The project will also determine whether this modelling framework can be combined with modern machine-learning methods to develop artificial agents capable of human level performance. Expected outcomes will include a practical methodology for rapidly generating models of effective human interaction that can be easily implemented in human-machine systems. This will provide a richer understanding of the fundamental perceptual-motor processes that support robust human interaction and enhanced the effectiveness of human-machine collaboration and training technologies.Read moreRead less
Tracing nature's template: using statistical machine learning to evolve biocatalysts. In this project new computational methods will be developed to design nature-inspired, biological catalysts for industrial purposes. Such methods will enable catalysts to be designed that can improve the effectiveness and environmental footprint of drug development, agricultural and specialist chemical production and environmental remediation.
Discovery Early Career Researcher Award - Grant ID: DE190101486
Funder
Australian Research Council
Funding Amount
$400,000.00
Summary
Animal groups as mobile sensor networks. This project aims to provide biologically inspired solutions to the problems faced by mobile sensor networks. Mobile sensor networks provide a powerful new tool in environmental monitoring and surveillance, however, designing them to be energy efficient while not sacrificing information detection remains a challenge. By immersing animal groups into dynamically changing virtual environments this project will design new efficient mobile sensor networks. The ....Animal groups as mobile sensor networks. This project aims to provide biologically inspired solutions to the problems faced by mobile sensor networks. Mobile sensor networks provide a powerful new tool in environmental monitoring and surveillance, however, designing them to be energy efficient while not sacrificing information detection remains a challenge. By immersing animal groups into dynamically changing virtual environments this project will design new efficient mobile sensor networks. The project is expected to provide solutions to mobile sensor network limitations, benefitting areas including robotics, environmental monitoring and defence.Read moreRead less
Modelling and simulation of self-organised behaviour in biological and bio-inspired systems. Understanding self-organised systems is fundamental in biology and bio-inspired engineering. The project develops sophisticated mathematical modelling techniques and high performance simulation methods for such systems. This will increase our capacity to explain complex biological behaviour and to produce reliable bio-inspired engineering solutions