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with good electrical engineering knowledge. Funding support We are offering home fees and UKRI minimum stipend. Further information Please contact Tao Yang ezzty@exmail.nottingham.ac.uk for further
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, you will explore water-gas shift chemistry to tailor sustainable carbon dioxide and hydrogen concentrations as the first step to produce sustainable fuels. During the project you will design manufacture
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sits at the cutting edge of materials science and solid mechanics, using real-world microstructure data to simulate thermo-mechanical behaviour at the grain scale. Your work will form a key part of a
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-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
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Dr Faye Smith, Professor Cristina McKean (Oxford University) and colleagues from the Children’s Public Health Service in North Tyneside Council. See the ECLS website for more information about the
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and clinical phenotype information. Using these datasets, you will undertake comprehensive strategies aimed at the characterisation and therapeutic targeting of medulloblastoma biology, focussing
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engineering knowledge. Funding support We are offering home fees and UKRI minimum stipend. Further information Please contact Tao Yang ezzty@exmail.nottingham.ac.uk for further information Closing date 16
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
engineering and nanotechnology, and it will require the realization of novel devices for wide range of applications such as photodetectors, optical modulators, optical amplifiers, lasers, biosensors etc. based
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Institute of Photonic Technologies to be undertaken within the College of Engineering and Physical Sciences at Aston University. Financial Support This studentship includes a bursary to cover the fees rate
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
generate vast amounts of operational and maintenance data, much of it remains fragmented and underutilized. Unlocking insights from this unstructured data could enable earlier fault detection, improved