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in device behaviour will be explored to minimize false positives Scalability and Real-world Deployment: This research project will address the scalability of the proposed detection system to handle a
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DoS: Dr. Juri Na (juri.na@plymouth.ac.uk ) 2nd Supervisor: Dr. Claire Adams (claire.adams@plymouth.ac.uk ) 3rd Supervisor: Dr. Emanuela Ercolano (emanuela.ercolano@plymouth.ac.uk ) Applications are invited for a three-year PhD studentship The studentship will start on1st Jan 2026 Project...
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materials with the high performance of liquid systems. Through innovative ultraviolet photopolymerisation, you will synthesise and optimise eco-friendly GPEs using varying polymer blends, solvents, and
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learning. The project involves a collaborative team, including a postdoctoral researcher and a PhD student, with specific objectives: Define and acquire a comprehensive database of high-quality video priors
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This self-funded PhD opportunity focuses on assured multi-domain positioning, navigation, and timing (PNT), integrating data from space-based, terrestrial and platform-based sources of navigation information into versatile benchmarks supporting development of a new generation of assured PNT...
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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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Supervisory Team: Prof Neil Sandham PhD Supervisor: Neil Sandham Project description: This project is focused on scale-resolving simulations (large-eddy and direct numerical simulation) combined
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together world-class expertise in textiles, materials, soft robotics, biomechanics, sports, healthcare, machine learning and AI, with globally leading industrial and academic partners. Your Project
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This project is a collaborative research effort between
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alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic