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candidate will explore programmable, AI-enhanced networking frameworks designed to meet the evolving demands of Industry 5.0. This includes developing novel architectures that combine Software-Defined
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innovation through applied research, bridging academia and industry. Students will have access to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered
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applied research, bridging academia and industry. Students will have access to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered electronics research
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, Cranfield fosters innovation through applied research, bridging academia and industry. Students will have access to state-of-the-art laboratories, hardware/software resources, and design facilities
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, resourceful PhD candidate, with knowledge in aircraft power systems and IT skills in MATLAB/Simulink and other related software. Applicants should have achieved or be expecting to achieve a 1st or a 2:1 in an
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the power of AI/ML and software-defined networking (SDN), and distributed learning methodologies, the research will focus on creating self-configuring, self-optimizing, and self-healing mechanisms for real
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. Experience with molecular dynamics software such as LAMMS is desirable. Experience with molecular simulation software is beneficial. To apply please contact Dr Siperstein - flor.siperstein@manchester.ac.uk
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modelling and simulation techniques and software packages would be an advantage. Programming skills in languages such as Python, C++, MATLAB, are desirable, as is an awareness of machine learning or other AI
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using force plates and strain gauges. Use of statistical analysis software packages. Funding Eligibility: This studentship is only available to students with settled status in the UK, as classified by
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plates and strain gauges. Use of statistical analysis software packages. Funding Eligibility: This studentship is only available to students with settled status in the UK, as classified by EPSRC