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combination of experimental testing and computational modelling (Finite Element Analysis) to create solutions that accelerate the safe deployment of hydrogen aviation technologies. This position is part of
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. If the later, then the overall non- biodegradable component would not be eliminated but would be reduced. 'Smart substitutions'. This explores the possibility that chemical substitutions that provide required in
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these standard beam shaping techniques becomes challenging for mm waves, as all the circuit elements must scaled down to a comparable size or smaller. Our approach is to instead use so-called Huygen’s
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complex metal structures. This opportunity is centred around improving manufacturing productivity with advanced laser-matter interactions control and optimisation. The PhD will advance our comprehension
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of unmodified and modified polymers. If the later, then the overall non- biodegradable component would not be eliminated but would be reduced. 'Smart substitutions'. This explores the possibility that chemical
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elements offers moderate strength and relatively high productivity compared to its highly alloyed counterparts. However, automotive aluminium alloys are susceptible to natural ageing at room temperature
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approaches, including principal component analysis and machine learning, to handle multivariate datasets. Prior experience in data science is not essential; the successful candidate will be supported through
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Matlab, Python and C/C++ are preferred. Strong interpersonal skills and the capacity to work and learn independently will be required. English Language IELTS 6.0 Overall (with no individual component below
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MATLAB/Python/R etc Previous research experience with healthcare datasets or electronic health records English Language IELTS 6.5 Overall (with no individual component below 6.5) or Swansea University
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catalysts. This EPSRC-funded project leverages cutting-edge X-ray spectroscopy to unlock how alkali elements can be harnessed to create powerful heterogeneous catalysts for CO₂ and hydrogen technologies. By