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Field
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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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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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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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-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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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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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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—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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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
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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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throughout the water/wastewater value chain, allowing for more informed and robust strategies toward sustainability and circularity. Skills/Experience Required: Process systems engineering, wastewater