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initiatives, and Cotutelle (Dual Doctorate) programmes in partnership with international universities. The BDC coordinates all centrally funded PhD scholarships at the University and engages with a number of
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electronics, with the associated high-speed sensing and gate driving methods. You will be offered one of the following three salary grades: Research Associate, Senior Research Associate, or Research Fellow
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manipulation research involving the development of modular soft robotics actuation/sensing technologies and/or high-resolution tactile sensor technologies. Work within a multi-disciplinary, multi-institute team
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, and electrothermal analysis and characterisation of power devices the associated high-speed sensing and gate driving chips. You will become part of interactive team of PhDs and Post-doctorate
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require you to spend time at a remote field site in Western Australia for a few months per year with other research team members. You will be responsible for fieldwork, data analysis and paper writing. In
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, Bristol and there is an operational requirement to work on-site for a proportion of the working week. If desired and with suitable arrangements in place, it is possible to fulfil part of the role remotely
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’. Specifically, it focuses on how these virtual spaces might affect people’s mental well-being and what the role of psychological processes related to one’s sense of self is in this context. The project aims
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opportunity to make a major impact on future RF device technology. Applicants should have postgraduate (PhD) experience in Physics, Materials Science or Engineering, with a good publication record. The position
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include quantum computing, chip-scale quantum sensing and quantum communications. The appointee will work with material platforms such as: Silicon Nitride, Silicon and the opportunity to explore other
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). Please also provide information for two referees (this does not have to be your PhD supervisor but should be able to talk directly to your research). The position will involve working with machine learning