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Field
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machine structures, together with AI-driven optimization frameworks for diverse applications while considering LCA metrics. The success of this project could serve as a model for other energy-related
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will contribute to the field by: Developing a conversational AI interviewer capable of conducting real-time adaptive interviews. Building an automated candidate ranking model based on interview
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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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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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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throughout the water/wastewater value chain, allowing for more informed and robust strategies toward sustainability and circularity. Skills/Experience Required: Process systems engineering, wastewater
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engineering to name a few. No prior experience is mandatory. Some knowledge of microstructural characterisation would be advantageous. A driven individual with an inquisitive mind. Contact: Informal inquiries
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comprehensive model of what tranquillity is, the factors that influence it and how to design for it. Attention to design contexts and design processes will be key to ensuring that useful measurements, methods and
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are often costly and time-intensive to generate experimentally in engineering applications. This project seeks to overcome these challenges by integrating modelling and monitoring to achieve fast, accurate
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analytical models, then informing a refined acoustic model taking both into account with data-driven approaches. This research is inherently multidisciplinary, lying at the interface of fluid and solid