Switchable and stereocontrolled photoredox catalysis. This project aims to develop new catalytic synthetic reactions for the rapid and more direct functionalisation of organic compounds under mild conditions with the use of visible light. An integrated experimental and computational approach will be used to design potent visible-light photocatalysts that retain the advantages of standard photoredox catalysis but with the added ability to intercept and, thus control, reactive intermediates in sit ....Switchable and stereocontrolled photoredox catalysis. This project aims to develop new catalytic synthetic reactions for the rapid and more direct functionalisation of organic compounds under mild conditions with the use of visible light. An integrated experimental and computational approach will be used to design potent visible-light photocatalysts that retain the advantages of standard photoredox catalysis but with the added ability to intercept and, thus control, reactive intermediates in situ. This will enable the control of stereochemistry in photoredox reactions – not possible with standard catalysts - and establish other useful synthetic transformations. These strategies will make it easier to prepare valuable classes of organic molecules – efficiently, safely, and cost-effectively.
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Early Career Industry Fellowships - Grant ID: IE230100040
Funder
Australian Research Council
Funding Amount
$447,127.00
Summary
Unravelling the genetics of Kangaroo paws for climate-resilient gardens. The project will produce the first DNA-anchored plant lineage map of Kangaroo paws and gain novel insights into their resilient growth characteristics. Using novel genome and data-driven strategies, this project addresses the knowledge gap around the genetic heirloom of more than 200 iconic Kangaroo paw varieties to speed up the breeding of new varieties with enticing leaf patterns and flower colour combinations. While unra ....Unravelling the genetics of Kangaroo paws for climate-resilient gardens. The project will produce the first DNA-anchored plant lineage map of Kangaroo paws and gain novel insights into their resilient growth characteristics. Using novel genome and data-driven strategies, this project addresses the knowledge gap around the genetic heirloom of more than 200 iconic Kangaroo paw varieties to speed up the breeding of new varieties with enticing leaf patterns and flower colour combinations. While unravelling the inheritability and breeding barriers, immediate industry adoption will boost horticultural breeding programs long-term. This project uses cutting-edge science to enhance industry capacity for providing new Kangaroo paws for climate-resilient urban green spaces on the national and international market.Read moreRead less
Creating new stochastic models to understand the evolution of gene families. This project aims to extend stochastic modelling techniques in order to develop mathematically rigorous and biologically relevant models for the evolution of gene families. The project expects to model evolutionary processes such as gene retention, duplication and loss, and the generation of new gene functions. The duplication and subsequent re-purposing of genes is thought to be a key mechanism for generating evolution ....Creating new stochastic models to understand the evolution of gene families. This project aims to extend stochastic modelling techniques in order to develop mathematically rigorous and biologically relevant models for the evolution of gene families. The project expects to model evolutionary processes such as gene retention, duplication and loss, and the generation of new gene functions. The duplication and subsequent re-purposing of genes is thought to be a key mechanism for generating evolutionary novelty. By applying these models to genome data, the project expects to be able to quantify the importance of these different evolutionary mechanisms. The project will strengthen collaborative links between researchers in stochastic modelling and molecular evolutionary biology.Read moreRead less
Managing infectious disease through partial wildlife social networks. This project aims to investigate the dynamics of the spread of infectious disease in wildlife, derived from incomplete information about contact networks. Infectious diseases in wildlife are difficult to track and control, because it is not feasible to monitor each individual in a population and know the contact network for a population. The project will create ways to best utilise incomplete observational data of contact netw ....Managing infectious disease through partial wildlife social networks. This project aims to investigate the dynamics of the spread of infectious disease in wildlife, derived from incomplete information about contact networks. Infectious diseases in wildlife are difficult to track and control, because it is not feasible to monitor each individual in a population and know the contact network for a population. The project will create ways to best utilise incomplete observational data of contact networks to develop robust predictions of disease spread and population fate, and to reliably predict the outcomes of management interventions. These robust prediction methods will provide better insights for conservation of Australian wildlife.Read moreRead less
Smart Irrigation: integrating UAV soil moisture maps & variable rate sprays. This project will develop a state-of-the-art precision irrigation system for optimising water use and crop yield. Specifically, a novel UAV soil moisture mapping system based on passive microwave satellite remote sensing technology at L-band will be developed for near-surface soil moisture mapping at accuracies and spatial scales currently not attainable. These soil moisture maps will then be merged with irrigation wate ....Smart Irrigation: integrating UAV soil moisture maps & variable rate sprays. This project will develop a state-of-the-art precision irrigation system for optimising water use and crop yield. Specifically, a novel UAV soil moisture mapping system based on passive microwave satellite remote sensing technology at L-band will be developed for near-surface soil moisture mapping at accuracies and spatial scales currently not attainable. These soil moisture maps will then be merged with irrigation water delivery models to calibrate for spatial variation in soil properties and/or correct errors in spatial variation of rainfall and evapotranspiration inputs. Ultimately the water balance predictions will be used for implementation of variable rate irrigation control at scales hitherto unattainable.Read moreRead less
Three-dimensional Bayesian Modelling of Geological and Geophysical data. The project aims to develop technologies enabling rapid informed decision-making related to the management of natural resources, including critical metals, copper and water. This new technology will support a greener future, securing our energy future, our access to clean water and reduce the mining footprint. Expected outcomes include an enhanced capability in interoperable, integrated three-dimensional geological and geop ....Three-dimensional Bayesian Modelling of Geological and Geophysical data. The project aims to develop technologies enabling rapid informed decision-making related to the management of natural resources, including critical metals, copper and water. This new technology will support a greener future, securing our energy future, our access to clean water and reduce the mining footprint. Expected outcomes include an enhanced capability in interoperable, integrated three-dimensional geological and geophysical modelling in order to predictively characterise sub-surface geology. The outcome will be an open-source forecasting dashboard enabling decision making while considering underlying risk related to resource extractions and management with significant benefits to the Australian society (lower emissions, clean water).Read moreRead less
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less