Benchmarking the neurophysiology of human cortex models in vitro. This project aims to improve human brain models in vitro by developing an analytical tool benchmarking biophysical similarities to the adult human cortex. This project expects to generate new knowledge by testing for the first time the theory that integrating sensory-like inputs and awake/sleep-like cycles of electrical activity in vitro may complete the maturation of human brain organoid models. It will also generate new methods ....Benchmarking the neurophysiology of human cortex models in vitro. This project aims to improve human brain models in vitro by developing an analytical tool benchmarking biophysical similarities to the adult human cortex. This project expects to generate new knowledge by testing for the first time the theory that integrating sensory-like inputs and awake/sleep-like cycles of electrical activity in vitro may complete the maturation of human brain organoid models. It will also generate new methods to simplify the analysis of multimodal path-clamping data (Patch-seq). Expected outcomes will facilitate research collaboration and the reproducibility of accurate experimental replicates of the human brain. This will provide significant benefits in the global race to understand human brain computation mechanisms.Read moreRead less
Bio-inspired Computing for Problems with Chance Constraints. Bio-inspired algorithms have successfully been applied to a wide range of optimisation problems. Uncertainties in real-world applications can lead to critical failures of production schedules or safe critical systems. Chance constraints model such uncertainties and allow to limit the possibility of such failures. This future fellowship builds up the area of bio-inspired computing for problems with chance constraints. It develops high ....Bio-inspired Computing for Problems with Chance Constraints. Bio-inspired algorithms have successfully been applied to a wide range of optimisation problems. Uncertainties in real-world applications can lead to critical failures of production schedules or safe critical systems. Chance constraints model such uncertainties and allow to limit the possibility of such failures. This future fellowship builds up the area of bio-inspired computing for problems with chance constraints. It develops high performing bio-inspired algorithms for stochastic problems where the constraints can only be violated with a small probability. The outcomes will lead to more effective and reliable optimisation methods for complex planning processes in areas of national priority such as mining and manufacturing.Read moreRead less
Memetic algorithms for multiobjective optimisation problems in bioinformatics. Many questions of paramount importance in life sciences can be formulated as optimisation problems but using just a single criterion can be misleading. This project will address this problem using multiobjective optimisation and leveraging Australia's investment in supercomputing with algorithms that mimic evolutionary processes in silico.
Predicting the movement speeds of animals. The project seeks to reveal how marsupials modify their movement patterns and speeds as they navigate risky environments, and show how movement contributes to vulnerability and resilience. Movement is central to animal behaviour and the survival of species, because it underlies feeding, mating and the ability to escape from predators. However, we lack a framework for predicting how fast animals should move through their habitats given their needs to con ....Predicting the movement speeds of animals. The project seeks to reveal how marsupials modify their movement patterns and speeds as they navigate risky environments, and show how movement contributes to vulnerability and resilience. Movement is central to animal behaviour and the survival of species, because it underlies feeding, mating and the ability to escape from predators. However, we lack a framework for predicting how fast animals should move through their habitats given their needs to conserve energy, avoid detection by predators and minimise risks of injury or death. This project aims to develop mathematical models to predict how fast animals should move and then test these predictions using native species of conservation concern. This is expected to extend the field of performance ecology as well as inform management strategies for vulnerable marsupials.Read moreRead less
Organic Bionics: Soft Materials to Solve Hard Problems in Neuroengineering. This project aims to combine innovations in organic conductors, nanotechnology, 3D biofabrication and neuroengineering to develop a bioelectronic system capable of wireless neuromodulation with unprecedented stability and precision. This project expects to generate new knowledge regarding the properties of materials that promote optical neuromodulation and new strategies to obtain long-term material stability in biologic ....Organic Bionics: Soft Materials to Solve Hard Problems in Neuroengineering. This project aims to combine innovations in organic conductors, nanotechnology, 3D biofabrication and neuroengineering to develop a bioelectronic system capable of wireless neuromodulation with unprecedented stability and precision. This project expects to generate new knowledge regarding the properties of materials that promote optical neuromodulation and new strategies to obtain long-term material stability in biological environments. The expected outcome is to generate new material design rules to facilitate wireless neuromodulation technologies in biomedical engineering. The project will position Australia as a leader in bionic devices by creating a new 3D bioprinting hub for low-cost fabrication of bioelectronic systems.Read moreRead less
Next generation photonic waveguide sensors enabled by machine learning. This project aims to establish the next frontier in photonic waveguide sensing, by using machine learning to shift the complexity out of conventional photonic-waveguide/optical-fibre sensors and into smart detection algorithms. The complexity and instability of multimode photonic waveguides, traditionally a hinderance to sensing, will be advantageously employed to train deep learning models for sensing. Expected outcomes inc ....Next generation photonic waveguide sensors enabled by machine learning. This project aims to establish the next frontier in photonic waveguide sensing, by using machine learning to shift the complexity out of conventional photonic-waveguide/optical-fibre sensors and into smart detection algorithms. The complexity and instability of multimode photonic waveguides, traditionally a hinderance to sensing, will be advantageously employed to train deep learning models for sensing. Expected outcomes include the creation of intelligent photonic sensors that can, in principle, measure any environmental parameter using any optical waveguide material. It will create new critically needed measurement capabilities for challenging harsh environments, such as extreme temperature and in-vivo biochemical sensing.Read moreRead less
Psychological and neurophysiological mechanisms underlying human interaction in musical contexts. Human interaction in musical groups is a culturally widespread activity that showcases the remarkable capacity for precision and creativity in interpersonal coordination. Although such activity has beneficial effects, including the facilitation of prosociality and positive therapeutic outcomes, the psychological and neurophysiological mechanisms that underpin an individual’s ability to coordinate wi ....Psychological and neurophysiological mechanisms underlying human interaction in musical contexts. Human interaction in musical groups is a culturally widespread activity that showcases the remarkable capacity for precision and creativity in interpersonal coordination. Although such activity has beneficial effects, including the facilitation of prosociality and positive therapeutic outcomes, the psychological and neurophysiological mechanisms that underpin an individual’s ability to coordinate with others remain poorly understood. This project aims to investigate these mechanisms by examining relationships between behaviour, social factors, and brain structure and function in naturalistic and laboratory settings. This will inform the assessment and treatment of clinical conditions that affect movement timing and social interaction.Read moreRead less
Development of methods and algorithms to support multidisciplinary optimisation. This project will aim to develop a number of novel and computationally efficient schemes to deal with the key challenges facing multidisciplinary optimisation. These advancements will allow us to solve a number of challenging and intractable problems in science and engineering.
The nature and consequences of environmentally-generated phenotypic variation in natural populations. The ambient environment can generate both heritable and non-heritable variation in individual traits, but the role of such variation in evolution is poorly understood. This project will use a powerful model organism, the Australian neriid flies, to elucidate the evolutionary implications of environmentally-generated variation.
Decoding the neural representation of objects in the human brain. Humans can effortlessly recognise thousands of objects in a fraction of a second. This essential capacity is an integral part of our daily lives that allows us to recognise our keys, our car, our friends and family. This project will elucidate how humans recognise objects by investigating the neural representation of objects in the brain.