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and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly
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in an increasingly volatile landscape and this PhD programme offers students the opportunity to study the strategic, organisational, and policy challenges facing defence and security institutions. It
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candidate would have experience with computational modelling and control of dynamical systems. Other useful skills include scientific programming (e.g., Python or Matlab), control system design, and
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multilayer printed circuit boards (PCBs). It draws from disciplines including electrical and electronic engineering, embedded systems, computer vision, and cybersecurity. The ability to verify hardware without
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projects that drive efficiency, enhance user experience, and contribute to the University’s wider transformation programme. About You You will hold a degree in IT or equivalent experience and a strong track
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Platform-based solutions and lead projects that drive efficiency, enhance user experience, and contribute to the University’s wider transformation programme. About You You will have a degree in IT or
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. There is flexibility to tailor the research to your strengths and interests. Funding This fully funded Connected Waters Leverhulme Doctoral programme studentship is sponsored by the Leverhulme Trust and
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: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
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research and strong written and oral communication skills, as we can complete existing strengths with targeted training. Funding This fully funded Connected Waters Leverhulme Doctoral programme studentship
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap