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
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