Detection and viability of waterborne pathogens using a gut-on-chip. This project aims to resolve a significant problem for water utilities. Microbial pathogens Cryptosporidium, norovirus and adenovirus are the main public health concern for drinking water in developed nations. Water monitoring is limited by the lack of fast, reliable detection methods and viability assays for these pathogens. This project will use a novel gut-on-a-chip to develop for the first time rapid infectivity assays for ....Detection and viability of waterborne pathogens using a gut-on-chip. This project aims to resolve a significant problem for water utilities. Microbial pathogens Cryptosporidium, norovirus and adenovirus are the main public health concern for drinking water in developed nations. Water monitoring is limited by the lack of fast, reliable detection methods and viability assays for these pathogens. This project will use a novel gut-on-a-chip to develop for the first time rapid infectivity assays for Cryptosporidium, norovirus and adenovirus. Significant benefits include improved diagnostics and water disinfection assays, improved water treatment and reduced costs with global impact.Read moreRead less
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less