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Field
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Project Overview This PhD project is part of an Innovate UK-funded research programme focused on developing a novel ammonia-fueled engine and generator set (genset) demonstrator for harbour and
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the solution of governing PDEs. - Train machine learning models to predict lifetime and failure based on loading and environmental histories. The PhD student will have access to world-class computing facilities
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/or modelling is essential. Experience in machine learning, computer vision, and computer programming is desirable. In addition, applicants should be highly motivated, able to work independently, as
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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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an independent impact assessment of potential climate interventions in the Arctic marine environment through laboratory experiments and computer modelling. The team will develop physical, climate and ecosystem
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follow the EngD in Model-Based Systems Engineering Programme. They will be based at a Leonardo site in the UK. Entry requirements: A minimum of an upper-class honours degree (2:1) or overseas equivalent in
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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
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science and translational research models SHIELD supports research on therapeutic strategies, novel antimicrobial materials, and experimental models that bridge laboratory discoveries to clinical
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As part of the Restoration Ecology and Dynamics (READY) Doctoral Focal Award, we invite applications to the following PhD project: Measurement and modelling of future persistence of restored
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essential to determine how these models can be made lightweight in terms of computational complexity, memory footprint, and energy consumption for deployment on edge devices or constrained gateways