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Field
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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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coalitions for delivering reliable, low-carbon energy services. Collaborating closely with UK Power Networks, SSE Energy Solutions, and the University of East London, you will develop robust economic Model
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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
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and use the results to iteratively refine devices and models. You should have the ambition to publish you work in top ranked international journals and at leading conferences and have a dedication to
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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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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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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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experimentation and finite-element modelling. Research themes would be flexible including green steel formability under the EPSRC ADAP‑EAF programme for automotive and packaging applications; or micromechanical
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cells or the tumor microenvironment reduces tumor growth and extends survival in preclinical models, underscoring their potential as dual-function therapeutic targets. To address this, we aim to develop
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to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a distribution of normal cardiac anatomy and function (including motion) from healthy subjects. By