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Field
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The project: We invite applications for a fully funded PhD studentship in the Solid Mechanics Group at the University of Bristol to work on the predictive modeling of hydrogen-induced damage in
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of detecting complex damage types in composite aircraft structures, such as delamination and lightning strike damage. Leveraging active thermography, the system will address limitations of traditional visual
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. A non-deterministic AI machine learning model for the identical task would not offer this demonstrability or, critically, the repeatability of classical algorithm-based systems. Furthermore, there is
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is characterised by complex and highly dynamic turbulent flows that define the performance and design of renewable energy systems and their infrastructure. This PhD project aims to enhance
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clearance level. The Engineering Doctorate Researcher will follow the EngD in Model-Based Systems Engineering Programme. They will be based at NPL. Entry requirements: A minimum of an upper-class honours
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exhibit complex two-dimensional structures whose performance is governed by subtle variations in layer stacking and nanoscale disorder—features that are difficult to resolve with currently available
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season properties (e.g. number, intensity) for lead times ranging from one to approximately six months in the latest generation of dynamical seasonal and decadal forecast models. Seasonal forecasts
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an increasingly complex development environment. Areas to consider that impact the modelling are: Framework Language Process How wide / how deep i.e. what do we model and why? How much provides a good answer i.e
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, osteosarcoma) cell and time of origin is debated but experimental studies in different models suggest possible in utero induction with postnatal initiation similar to leukaemia. In Ewing sarcoma, FET::ETS gene
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learning algorithm to develop an ability to choose what main data pattern/structure to preserve? This PhD project will approach this question by developing modelling strategies and pipelines to enable human