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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big
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early ‘prodromal’ stages) is yet to be established in large community settings. This PhD project will examine the effectiveness of AI-based analysis of eye images in predicting cognitive/neurodegenerative
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, the supervision team have obtained data access to indoor environment sensor data at national scale from a leading industrial collaborator. To pair with this big dataset, outdoor environment data at MetOffice can be
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, including: Genomic technologies – hands-on experience in long-read sequencing and variant interpretation Bioinformatics – pipeline development, visualisation, and statistical modelling PRS – applying big data
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sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data
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from which a continuous emulsion flow and recycled gas flow is taken. The work will include the design and scaleup and perhaps the construction of a large-scale system which can be tested at ground
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the design of the nozzle. The work will include the design of a scale- up dispersion system and perhaps the construction of a large-scale dispersion system which can be tested at ground level. There is no
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to air pollution in the future, and planning further policy changes. This PhD project will develop statistical modelling frameworks that are able to handle large-scale, complex, and correlated time series
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This is an exciting opportunity to explore the role of complex microbial communities in promoting resilience and maximising yields in large scale algal bioreactors exposed to different environmental