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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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applied metrology and robotics. Vision This project addresses the challenge of rapidly and autonomously generating high-precision 3D reconstructions of large-scale complex assets. Uniquely, it integrates
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-ray Computed Tomography (XCT) has evolved into a significant "big data" challenge, with a single scanner easily generating over 10TB of data annually. The sheer volume of this structured data creates
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A large volume of data are routinely captured in high-performance training and competition environments. Whilst these data have the potential to inform key performance decisions, the full potential
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education regulations, and the European campus of Northeastern University – a large, top-tier research intensive, Boston based institution. Founded in 1898, Northeastern received $230.7m of external research
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Rapid situational awareness is critical when health systems are overwhelmed by major incidents, including Chemical, Biological, Radiological and Nuclear (CBRN) events and large-scale mass casualty
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, utilising high-performance computing (HPC) servers and data from large-scale facilities such as Diamond Light Source and ISIS Neutron and Muon Source. For informal enquiries please contact Dr Jay Warnett
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from NUIT) for safe and secure data storage of large bioinformatic datasets and ultimately deposition in online repositories prior to publication. Create and maintain useable and shareable version
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Modelling, Applied Statistics, Linguistics, Data Analysis, Large Language Models, Machine Learning. Start date: 1st October 2026 Deadline: 30th April Duration: 36 months Funding: Funded Funding towards