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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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of large-scale soil and environmental data). By improving the water use efficiency of crops (particularly in dryland areas), the use of surfactants will reduce irrigation demand, whilst maintaining crop
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algorithms are used that allow a computer to process large data-sets and learn patterns and behaviours, thus allowing them to respond when the same patterns are seen in new data. This include 'supervised
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economy and de-centralised manufacturing. To enhance process efficiency (clean) by researching sensors and big data for factory management, product service systems, factory modelling and artificial
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CFD-FEA Combined Thermal-Fluid-Mechanical Modelling for Defect Control in Additive Manufacturing PhD
information, it falls short in providing stress-related data, while the finite element analysis (FEA) is widely used for determining residual stress and distortion, but it has great uncertainty in predicting
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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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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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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