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user studies, develop novel algorithms, build immersive/augmented realities, and validate your solutions in real-world settings. This PhD is ideal for candidates interested in one or more of the
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user studies, develop novel algorithms, build immersive/augmented realities, and validate your solutions in real-world settings. This PhD is ideal for candidates interested in one or more of the
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out together with seven industrial partners and is externally funded by the Knowledge Foundation. In co-production with our corporate partners and the community, we develop concepts, principles, methods
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Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing power, latency, and memory bandwidth issues with reliable
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fabrication facilities as well as high performance computing (HPC) facilities at QUT. PhD2: Pore-network modelling of reactive transport As a PhD student, you will develop efficient pore-network modelling
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package totalling approximately $47,000 per annum tax exempt (2025 rate) a four-year Research Training Program (RTP) Fee-Offset a four-year project expense and development package of $13,000 per annum a
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algorithm that interface with interpolated geotechnical fields to generate adaptive, variable-length stope geometries. Calibrate and validate the developed models using real-world mine data Develop, document
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centre led by SINTEF Energy Research. FME NorthWind advances research and innovation for the sustainable development of wind energy, with particular emphasis on balancing technological deployment with
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centre led by SINTEF Energy Research. FME NorthWind advances research and innovation for the sustainable development of wind energy, with particular emphasis on balancing technological deployment with
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decisions, and to study how agentic reasoning can navigate trade-offs between interpretability, statistical reliability, and computational feasibility – and to develop principled criteria for when a given