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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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by the Ada Lovelace Centre and the University of Birmingham. This interdisciplinary project is ideal for candidates with a background in physics, materials science, chemistry, or computational science
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candidates with a background in meteorology, climatology, physics, and any related discipline, and a strong interest in applying advanced physical and computational methods to real-world challenges in the area
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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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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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the solidarity and cooperation within and between democratic countries. There have been concerns about the use of Generative AI and Large Language Models (LLMs) as tools for the dissemination of disinformation and
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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
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-edge biological modelling to understand exactly how IIDs spread in nurseries. The project aims to develop improved intervention guidelines to prevent high mortality IIDs, considering what’s realistic and
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins
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benchmarking (Frank et al., arXiv:2024) using blind testing approaches. The student will develop verification-based benchmarking approaches for digital quantum computers with realistic noise models, leading