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Field
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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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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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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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Anti-cancer drugs can be classified into small- and large-molecule drugs. One of the more complex types of novel anti-cancer drugs are antibody-drug conjugates (ADCs), in which several molecules
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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
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Project summary: Project examines whether Large Language Models (LLMs) embed systematic political ideologies that influence their financial judgements. Extends a novel auditing framework to measure
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to better predict the right medication for patients, using data from multiple large biobanks. The position is based in the multi-disciplinary and collaborative Wray-Visscher Complex Trait Genomics Group
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Health emergencies, mass casualty events and disasters generate large volumes of operational, clinical and organisational data. However, these data are rarely synthesised into structured, analysable