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stresses. Based on the experimental data, a semi-empirical model to be developed to assess insulation degradation and identify failure signatures that can inform future predictive asset management strategies
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and accuracy, ultimately saving lives. This collaborative PhD project aims to develop and evaluate advanced deep learning models for speech and audio analysis to predict Category 1 emergencies
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(e.g., in the context of risk, uncertainty, future thinking), social processes (e.g., interpersonal dynamics, social norms, and influence), and behavioral outcomes (e.g., behavior change, social action
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) offer new avenues to tackle this problem. AI models have demonstrated strong potential in clinically relevant insights from electrical signals such as ECGs, and from cardiac imaging modalities including
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for healthcare. A2 Project or subject specific skills; e.g. experience of data collection, the use of AI/ML tools for processing collected data and understanding of hardware technologies to configure systems and
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reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter
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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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drives tumour development, childhood cancers lack the extended time frame needed to accumulate the mutations required for tumorigenesis by those routes. Therefore, endogenous mutagenic processes are a
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processes. Objective 2: Develop methods to measure and assess interoperability, helping identify where and why issues arise. Objective 3: Create solutions that address interoperability challenges and meet
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, which are essential for safe operation in these challenging aerospace environments. You will develop robust, physics-based models to analyse failure, with a focus on understanding mechanical and