30 embedded-system "https:" "https:" "https:" "https:" "IFM" "IFM" "IFM" PhD scholarships at University of Warwick
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Research area and project description: AI data centres are digital engines, yet ~30% of energy is wasted as heat in power conversion and distribution. Directly addressing the UK’s Clean Power 2030
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on the design and modelling of ultra-high voltage IGBTs, thyristors, and SiC devices for HVDC and energy systems. Embedded within the EPSRC Rewire network, the project strengthens UK capability in high-voltage
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candidate to join the UKRI Future Leaders Fellowship (FLF) research project of Dr. Zsuzsanna Koczor-Benda on “Quantum embedding for functional nanodevice design” in the Department of Chemistry at
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this research area to https://www.warwick.ac.uk/engineeringscholarships/ng_epsrc/app via the above 'Apply' button. If this initial application is successful, we will invite you to submit a formal application
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their skills and interests in this research area to https://warwick.ac.uk/fac/sci/eng/postgraduate/funding/ot_epsrc/app/ via the above 'Apply' button. If this initial application is successful, we will invite
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validation methods employed for battery systems created for electric vehicles, aerospace or stationary storage. This PhD will aim to deliver a new validated methodology for scientifically assessing lithium-ion
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Sponsor/ Supporting Company: Johnson Matthey Research Group: Advanced Steel Processing Group Eligibility This project is funded via the UKRI IDLA sponsored by Johnson Matthey with the University
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manufacturing, yet their performance is fundamentally limited by our inability to precisely control particle alignment and microstructure during fabrication. Existing methods—such as magnetic or electric field
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improving the accuracy and reliability of lifetime predictions for demanding applications. The project is sponsored by the Dutch Polymer Institute (DPI), a leading international research platform that brings
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designed specifically for the spatial and sequential structures inherent in XCT projections and reconstructed volumes. The goal is to achieve a 60-80% reduction in data size without compromising