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Field
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PhD project: Modelling Resilience of Water Distribution Networks Supervised by Rasa Remenyte-Prescott (Faculty of Engineering) Aim: To develop an modelling approach for assessing water network
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. The aim of this project is to develop multimodal generative AI for embodied collaborative agents. This project aims to create AI agents capable of seamlessly collaborating with humans and other agents in
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affected by several factors. As a result, the decision made based on these images could be affected depending on the quality of the images. For this reason, the aim of this project is to propose a
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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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methods: The Met Office aims to help its customers stay safe and thrive by producing reliable weather and climate information. Numerical models of the climate system are an important tool to help achieve
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and model inaccuracy. This role is dedicated to solving this problem, building an embedded plume-in-grid contrail model for use in an operational weather model - supporting technological solutions
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(e.g. neural ODEs and SDEs), identifiability and interpretability, large language and sequence models, and multimodal data integration. This position will be based at the world-leading CRUK Cambridge
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et al. 2022; Mainstone et al. 2018; Datry et al. 2016). Streams are often flagged as intermittent based on downstream flows, but where or how they dry is not known. Capturing the evolution of river
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position aims to conduct holistic modelling and analysis of integrated energy systems to reach optimal system performance while incorporating various sustainable energy infrastructures. Potential research
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of architecture standards and inform training and development of the systems modellers of the future. Programme and Location: The Engineering Doctorate Researcher will follow the EngD in Model-Based Systems