65 web-programmer-developer-"https:"-"https:"-"https:"-"https:" positions at Newcastle University in United Kingdom
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Industries: Net Zero (PINZ) . The PINZ CDT will train the next generation of process and chemical engineers, and chemists, to develop the new processes, process technologies and green chemistries required
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aims to develop novel materials and components that facilitate strong light-matter interactions and enhance nonlinear optical responses for advanced photonic functionalities. This project is multifaceted
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. There will be scope for both observational and theoretical work, as we develop ever more sophisticated reverberation mapping models that account for general relativistic and radiative transfer effects, and
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-efficient and high-performance photonic devices have been driven by the quantum revolution. This PhD studentship aims to develop novel materials and components that facilitate strong light-matter interactions
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-of-the-art AI and computing facilities, receive tailored training and professional development, collaborate with experts across disciplines, and contribute to open-source tools that advance the wider AI
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, or computational strategies to your interests—whether that involves large-scale reservoir simulation, pore-scale physics or supercritical CO2 behaviour. The project will be developed within a vibrant research
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biodegradable and sustainable alternatives are urgently needed. This project will develop a new class of biodegradable, polysaccharide additives for use in laundry formulations, with a focus on performance
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. Overview This PhD project is part of the EPSRC Centre for Doctoral Training in Process Industries: Net Zero (PINZ) . The PINZ CDT will train the next generation of process and chemical engineers, and
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charcoal from historic fires with known ages (years to decades to 174 MA old) to examine compositional evolution and preservation over geological time. By comparing these with freshly engineered biochars, we
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remain limited due to a lack of systematic comparisons and underused legacy datasets. This project will develop a framework to predict sediment properties directly from geophysical data. Legacy SI data