167 structures-"https:"-"https:"-"https:"-"https:"-"https:" PhD positions in United Kingdom
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to develop novel multifunctional polymer-based composites for (soft) robotics, focusing on bio-inspired tactile sensing skins, actuators, and adaptive structures. By creating lightweight, stretchable, and
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an adhesive heavy application, and to find methods for End-of-Life disassembly. Battery pack construction uses adhesives to fix multiple cells in place, creating a block of battery modules. The adhesive has
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: How sensitive are the emission factors of the different stoves to user behaviour, and what are the underlying mechanisms? What are the emissions profiles of solid waste (e.g. construction timber, garden
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to long-term responsibility This studentship is expressly structured to provide a strong pathway into industry, positioning successful candidates favorably for future roles with Jungbunzlauer. What you
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components for fusion reactors. These technologies enable highly precise, modular, and geometrically flexible fabrication methods suited to the structural and functional requirements of fusion devices
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modifiers to mitigate this problem, but many of the existing additions have been discovered by chance. To allow more control of structure during the solidification stage as the amount of recycled content is
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climate change-resilient infrastructure slopes. This PhD project will investigate the impact of different intervention types (e.g. structural measures, modified materials, vegetation, drainage, and/or cover
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and widely used technique for probing molecular structure, dynamics, and chemical processes across the physical, chemical, and life sciences. In this project, NMR will be coupled with computational
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The PhD studentship will be based at the University of Cambridge in the Department of Materials Science and Metallurgy as part of the Structural Materials Group. The Structural Materials Group is a
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(Studentship code MSP108) Data structures and postprocessing for phylogenetic MCMC (Studentship code MSP109) Time-changed MCMC for complex sampling (Studentship code MSP110) Statistical learning for extreme