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Martin Australia invite applications for a project under this program, exploring the development of Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather
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seismic data to produce 3D geophysical models that will constrain the subsurface extent of rock units that might be suitable for hydrogen generation via radiolysis and/or serpentinization and identify
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their joints using our statistical shape and appearance model. We aim to better understand how alterations in joint mechanics affect the morphology and composition of human joints – some will thrive and others
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are recruiting four PhD students for the following Projects: Project 1: Unifying on-farm data and crop models to enhance tactical crop decisions Summary: Despite the increasing availability of on-farm data and
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injection into a reservoir saturated by water from core scale up to the numerical cell or a reservoir is a challenge. For immiscible two-phase flow in layer-cake reservoirs, the models for pseudo relative