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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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” highly flexible components and ensure their energy harvesting capabilities. This project will contribute to ambitious plans for SBSP as a vital part of the future Net Zero landscape. Recent advances in
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environment for all PGRs creating a strong sense of community across research disciplines. Community and research culture is important to our PGRs and the FoE support this by working closely with our
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life-cycle analysis, GasTurb/PyCycle/NPSS, and CoCip is preferred Excellent written and verbal communication skills[AR1] is preferred. To apply, please contact Dr Eastwood - david.eastwood
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The student will benefit from working alongside a multidisciplinary team of engineers, mathematicians, and physicists at the University of Manchester as well as a wide collaboration network within the UK and
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student community and maintain a culture where everyone can work and study together harmoniously with dignity and respect. This is reflected in our University values of ambition, impact, respect and
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to industrial clients such as Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD, Bombardier, QinetiQ, Thales, Network Rail, Schlumberger and Alstom. Entry requirements A minimum of a 2:1 first degree in a
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community across research disciplines. Community and research culture is important to our PGRs and the FoE support this by working closely with our Postgraduate Research Society (PGES) and our PGR Research
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for patients falling between primary and secondary NHS care. The successful candidate will join the research community at the Institute of Mental Health (IMH), Nottingham; and the multi-disciplinary team working
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network and enhancing their career prospects. From this experience, the student will gain a wide range of transferable skills that will significantly enhance their employability. They will develop strong