60 embedded-system-"https:"-"https:"-"https:"-"https:"-"UCL" PhD positions at University of Nottingham
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, altermagnets, and new classes of compensated spin-split systems. These materials exhibit magnetic order without conventional ferromagnetism, offering new routes to functional behaviour rooted in crystal symmetry
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electric motor manufacturing platforms at both locations. Project Description Electrification is a main enabler for decarbonised transportation. Ambitious roadmaps to achieve the “Net Zero” target by 2050 in
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lab is challenging the traditional view of soil-structure interaction (SSI). This project will investigate the critical role of changing particle shape on material wear and elevated stress transfer
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multidisciplinary effort to investigate the anatomical, physical and cellular factors that shape internal root environments. The project will explore how root organisation and environmental conditions combine
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to the interests of one of the School’s research groups: Cyber-physical Health and Assistive Robotics Technologies Computational Optimisation and Learning Lab Computer Vision Lab Cyber Security Functional
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Join a fully funded, industry-sponsored PhD at the University of Nottingham (Mechanical & Aerospace Systems research group), in partnership with the Manufacturing Technology Centre (MTC). You will
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. Supervisors: Ian Sayers, Cathy Merry (Nottingham), Gleb Yakubov (Leeds), David Thornton (Manchester), Luke Bonser (AstraZeneca) Chronic sputum production is debilitating and a feature shared by several
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We invite applications for a PhD project focused on fundamental research into novel low-emission ammonia combustion/oxidation processes. This position is based within the Faculty of Engineering at
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for at least two years of the project. At Diamond, the student will be able to utilise cutting edge equipment to study single atom catalysts anchored on defective graphene substrates. Catalysis is a key
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(CHF), tailored to complex geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow