51 web-programmer-developer-"https:"-"UCL"-"CEA-Saclay"-"https:"-"https:" positions at Newcastle University in United Kingdom
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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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-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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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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. 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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discrete element method (DEM). The research outcomes will provide critical insights into the microscale transport phenomena relevant to subsurface storage performance, including permeability evolution and
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known as ‘brittle bone disease’ is a genetic condition which affects the development of type I collagen. Patients with OI have poor mineralisation of their bones and consequently are much more likely to
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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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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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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