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Field
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effectively under increasingly demanding operating conditions. Thermal management based on direct contact two-phase boiling dielectric oil immersion strategies offer a transformative route, using liquid-vapour
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synergistically, similar to how systems work in nature. Experimental work will include the following indicative activities: Designing and developing CAD models of test coupons and other structures Fabrication
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be developing advanced spatial models such as graph-based approaches and network analytics to predict how blue network dynamics, fragmentation and surrounding land use interact to shape ecosystem
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strength-to-weight ratios, making them central to the development of a more sustainable industry. However, under extreme environments and loading conditions (high strain-rate, temperature), these materials
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Learning heat: Physics-Informed Fourier Neural Operators for High-Fidelity Thermal NDE Modern non-destructive evaluation (NDE) increasingly relies on AI models that can reason with physics, scale
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regulator of atmospheric CO₂. Emerging studies show that MNPs can impair nutrient uptake, photosynthesis, and growth in algae, while also reshaping community composition by favouring opportunistic species
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Primary supervisor – Prof Parvadha Suntharalingham BACKGROUND The ocean plays a key role in controlling atmospheric greenhouse-gas levels. It removes a significant fraction of anthropogenic carbon
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on extreme weather, agriculture, water resources, hydropower and health in affected regions. A delayed start to the monsoon season (monsoon onset) occurs in future climate projections of several monsoon
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also actively remove CO₂ from the atmosphere. One promising idea is the restoration and enhancement of seabed “blue carbon” stores. Yet, the critical question remains: can seabed sediments reliably
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. This PhD proposal aims to develop an integrated modelling-prediction-control framework that uses extreme-weather-aware AI to coordinate frequency stability, voltage control, optimal power distribution, and