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NbS including treatment wetlands (TWs) and sustainable drainage systems (SuDs) at water recycling centres and within individual surface water catchment areas, respectively. These systems offer
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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This is a self-funded opportunity relying on Computational Fluid Dynamics (CFD) and wind tunnel testing to further the design of porous airfoils with superior aerodynamic efficiency. Building
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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increasingly important. The aim of the project is to explore the collaborative dynamics of agents within eCPS, with a specific focus on aligning their behaviours towards achieving sustainability goals. Cranfield
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
experts in the field, contributing to a dynamic, research-led environment. This project is sponsored by Rolls-Royce, a global leader in aerospace and defence innovation. The sponsor brings deep domain
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equivalent in a related discipline. Funding This is a self funded opportunity. Find out more about fees here. Diversity and Inclusion at Cranfield We are committed to fostering equity, diversity, and inclusion
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. Funding This is a self-funded PhD. Find out more about fees. Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and
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) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the