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to the development of multiscale computational models for simulating crack propagation and establishing reliable methods to predict the residual strength of composite structures. The simulations, performed in Ansys
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to short-term spatiotemporal extreme wind speed prediction, addressing limitations in conventional models. The candidate will: Analyse Extreme Data Distributions: Apply advanced statistical techniques
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provide a key tool to characterise mechanistic changes and assist in predicting the human health impact of exposure to different air pollutants. The aim is to use multi-omic approaches to characterise
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of photons from an ensemble of atoms, and time-crystalline ones, marked by emergent self-organised oscillations. Beyond their fundamental academic interest, these phases are also receiving particular
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PhD Studentship: Experimental Investigation of Pharmaceutical Tablet Disintegration and Drug Release
tomography, Raman spectroscopy) to map 3D drug distribution inside tablets. Measuring real-time disintegration under simulated stomach conditions. Analysing drug release data to refine predictive models
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practical knowledge of protein purification, ensemble biochemistry, and sample preparation to support your biophysical studies; • interact with biochemist and structural biology collaborators based
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challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction
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processes. Carbonate biomineralisation is a key process in global carbon cycling, but there are major gaps in our understanding of how biominerals form. We lack a quantitative understanding that can predict
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challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction
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Project Title: Characterising clonal dynamics of somatic mutations in vivo for early prediction of carcinogenicity using advanced error corrected next generation sequencing Supervisor: Dr Alex Cagan