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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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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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targets the development of advanced coatings to prevent cell-to-cell propagation during runaway events. It combines experimental studies, numerical modelling, and real-world burner rig testing, culminating
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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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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
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verification methodology and corresponding toolchain to detect and mitigate such threats to CPS at the design time making the CPS resilient-by-design. Typically, CPS are modelled as hybrid systems, comprising
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of the complex physics governing the interaction between the heat source and the material. Additionally, it seeks to develop an efficient modelling approach to accurately predict and control the temperature field
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. Using gastruloids as a model system with which to study GAG structure/function relationships. Generating gastruloids from induced pluripotent stem cells (iPSCs) to create in vitro models for studying
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for downstream tasks. In this project, you will develop novel unsupervised machine learning methods to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a