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Field
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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(e.g. Visudyne) are released predominantly by partitioning, typically resulting in rapid redistribution. Experimental investigation of drug release in complex physiological media such as serum remains
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the framework of the contract. This doctoral project lies at the intersection of geography, economics, statistical physics, and complexity science. It examines the relationships between residential locations and
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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state including CRY1/2, PER, CK1 kinase and other proteins, and a late repressed state where only CRY1 remains associated with CLOCK/BMAL1. Additionally, cytosolic CRY/PER/CK1 containing complexes
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of energy and computational power. Reservoir computing offers a promising alternative by using complex physical systems to perform tasks such as prediction, classification, and signal processing. However, one
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Identifying and validating models for complex structures featuring nonlinearity remains a cutting-edge challenge in structural dynamics, with applications spanning civil structures, microelectronics
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you often find yourself fascinated by the multiscale complexity of real-world engineering problems? In this project you will develop advanced multiphase transport models for porous systems, bridging
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). Together, they will provide training to generate findings with potential to reshape NHS hospital care. The PhD aligns with NHS priorities around integrated care for people with complex needs. The project
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and Australian campuses that mobilises expertise to address the complex challenges faced by rapidly urbanising cities in the Global South. The Hub brings together Monash multidisciplinary researchers