65 postdoc-in-thermal-network-of-the-physical-building PhD positions at Newcastle University
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engineering, physics and applied mathematics. You should have experience in one or more of the following: numerical methods, high-performance computing (HPC), Computational Fluid Dynamics (CFD), applied
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the structure and robustness of the ecological networks supporting reef fish communities at different positions along depth, latitude, and longitude gradients; challenging these networks under hypothesized future
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potential to shape policy tools for a more sustainable and resilient future. What’s in it for you? Network widely: collaborate across government, academia and industry, including leading water sector
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serious safety risks for older adults, and rapid, reliable detection can significantly reduce long-term injury and improve emergency response. Building on recent advances in vibration-based sensing
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electricity and water to run washing machines. Improving the sustainability of these everyday processes is essential for meeting net-zero targets and reducing environmental impact. High-performance laundry
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geomorphic and hydrological processes that create a variety of habitats. This study will be mainly centered on the Mar floodplain near Braemar in the Cairngorms National Park and will build on earlier
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and improve emergency response. Building on recent advances in vibration-based sensing, this PhD will explore how the structural and material characteristics of floor systems influence the vibration
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they replicate the responses seen in people. 3D bioprinting techniques are an exciting set of fabrication technologies which build on the principles of 3D printing, but which can process cells and other biological
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benefits. This exciting project, in partnership with the globally leading agrochemical company Syngenta, will develop and apply new organosodium reagents in organic synthesis. This will build upon our recent
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erode trust in model outcomes. This project aims to create a unified framework for building lightweight, data-efficient multi-modal AI models that can effectively handle uncertainty and generate reliable