44 phd-in-architecture-interior-design-built-environment PhD positions at Cranfield University in Uk
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Cranfield University invites applications for a PhD funded by Thames Water through the Ofwat Innovation Fund. The studentship covers full Home tuition fees plus a tax free stipend of £24,000 per
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. This project leverages and advances these trends, targeting the development of multi-functional coatings to enhance EV battery safety. This PhD will design and characterise intumescent, dielectric, lightweight
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This self-funded PhD opportunity focuses on assured multi-domain positioning, navigation, and timing (PNT), integrating data from space-based, terrestrial and platform-based sources of navigation
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diagnostics, novel thermodynamic cycle performance, etc., which have been built up over the last half century. This provides a unique capability to assist researchers and engineers in new power plant designs
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investigate strategies to enhance communication security, focusing on resilience against jamming and spoofing attacks. Students will work on designing secure architectures that ensure data integrity and system
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Embark on a thrilling, fully funded four-year PhD journey, with an enhanced stipend of £25,726 per year, and deliver new evidence on how nature-based solutions can reduce the occurrence and mitigate
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Nuclear fusion offers the prospect of clean, abundant, and safe energy that could transform global energy systems. Achieving this goal depends on materials that can endure extreme environments
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more sensitive and faster cancer imaging. This PhD project will focus on surface functionalisation of metascintillators to optimise their scintillation performance, light yield, timing resolution, and
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reliability. Embedded Redundancy and Self-Healing: Design systems with built-in redundancy and self-healing capabilities that allow for automatic recovery from faults without human intervention. Cranfield
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industries, including transportation, consumer electronics, and industrial automation. This PhD project focuses on the design and optimization of intelligent systems with an emphasis on energy efficiency and