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Field
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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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, multi-source learning is used to integrate diverse patient populations to build robust models, but having to protect sensitive information. Various modern ML paradigms are proposed to address the diverse
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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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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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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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mitigation strategies to prevent performance losses due to these impurities. We will explore both experimental techniques as well as computational models to provide feedback for designing higher efficient
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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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noise models, leading to metrics devoid of assumptions about noise impacts (e.g., cross-talk or non-Markovian noise in gate fidelities). As shown by the supervisory team, non-Markovian noise can be a
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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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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