Industrial Transformation Research Hubs - Grant ID: IH210100051
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
The ARC Research Hub for Digital Bioprocess Development. The ARC Hub for Digital Bioprocess Development aims to assist the Biopharma industry by increasing digital innovation, productivity and competitiveness. An interdisciplinary team of engineers, scientists and computing specialists will develop digitally integrated advanced manufacturing processes and a platform for industry adoption. The program will address key bioprocessing research challenges and develop new process and digital models th ....The ARC Research Hub for Digital Bioprocess Development. The ARC Hub for Digital Bioprocess Development aims to assist the Biopharma industry by increasing digital innovation, productivity and competitiveness. An interdisciplinary team of engineers, scientists and computing specialists will develop digitally integrated advanced manufacturing processes and a platform for industry adoption. The program will address key bioprocessing research challenges and develop new process and digital models that can predict and optimise manufacturing processes, resulting in greater yields, faster and more flexible processes and enhanced product stability. The Hub will transform biopharmaceutical manufacturing and unlock growth opportunities to forge an internationally competitive Australian Biopharma sector.Read moreRead less
Fractional dynamic models for MRI to probe tissue microstructure. This project aims to develop new mathematical tools for mapping tissue microstructural properties via the use of space-time fractional calculus methods. In magnetic resonance imaging, mathematical models and their parameters play a key role in associating information between images and biology, with the overall aim of producing spatially resolved maps of tissue property variations. However, models which can inform on changes in mi ....Fractional dynamic models for MRI to probe tissue microstructure. This project aims to develop new mathematical tools for mapping tissue microstructural properties via the use of space-time fractional calculus methods. In magnetic resonance imaging, mathematical models and their parameters play a key role in associating information between images and biology, with the overall aim of producing spatially resolved maps of tissue property variations. However, models which can inform on changes in microscale tissue properties are lacking. The tools developed by this project will be used to generate new magnetic resonance image based maps to convey information on tissue microstructure changes in the human brain. Additionally, the mathematical tools developed will be transferable to other applications where diffusion and transport in heterogeneous porous media play a role.Read moreRead less
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less
Advanced materials for space propulsion: satellites and cubesats. Poorly controlled interactions between plasmas and surfaces often mean loss of process efficiency and surface degradation over time. For Hall thrusters, a type of engine used to move satellites in space, this means increased fuel consumption and shorter useful life. Through modelling and experiment, this project will show how intelligent selection of advanced materials and plasma parameters can minimise surface wear, enable in sit ....Advanced materials for space propulsion: satellites and cubesats. Poorly controlled interactions between plasmas and surfaces often mean loss of process efficiency and surface degradation over time. For Hall thrusters, a type of engine used to move satellites in space, this means increased fuel consumption and shorter useful life. Through modelling and experiment, this project will show how intelligent selection of advanced materials and plasma parameters can minimise surface wear, enable in situ material repair to extend device lifetime, and modulate plasma properties to increase thruster efficiency for a given task. These benefits enable reliable propulsion platforms for massive communication and observation satellite networks and deep space exploration.Read moreRead less