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other words, they are ineffective in understanding that correlation does not imply causation. This design flaw is clearly exemplified by recent large language models such as ChatGPT that are able to mimic
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used to predict armour performance against threats, known and emerging. Overview Initially, the successful candidate will identify the critical data capture steps for accurate prediction of material
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useful life of critical assets. Currently, Artificial Intelligence (AI) based big data analytics tools have been massively developed in the research community to address this challenge. These AI-based
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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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offers a rapid inspection while covering a large area within a short time. Therefore, it has been successfully applied to a wide range of areas such as civil engineering, medicine and biology as
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coefficients. This strategy carries large uncertainty and requires vast amount of expensive and time-consuming experimental data. Worse, sometimes the experimental data is simply inaccessible. The need for cost
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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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from energy facilities. The proposed research will test and develop new and/or improved atmospheric modelling techniques that can be coupled with existing observations of methane, and other data streams