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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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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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November 2025 or as soon as possible thereafter. This PhD project aims to explore how emerging datasets could provide value to the UK’s insurance industry through a combination of data analytics, modelling
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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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to work with an international team on developing cutting-edge novel demographic, statistical and computational methods in estimating, modelling and forecasting measures of health, well-being, and human
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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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interdisciplinary research environment is highly desirable but not necessary as training will be provided. Experience of computer modelling is desirable but not essential as full training will be provided in
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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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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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determine the impact of community acquired pneumonia that requires hospitalisation has on the quality of life of patients. The final stage will be to design a generic economic model to evaluate any new