14 systems-science "https:" "https:" "https:" "https:" "University of Exeter" positions at University of Plymouth in United Kingdom
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) for offshore applications and renewable energy, including wind and tidal systems. It overcomes limitations of subsea cabling—cost, maintenance, and environmental vulnerability—by exploiting electromagnetic
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, biochemistry, neuroscience, electrical/electronic measurements and finite element analysis/COMSOL. Applications from both UK and overseas students are welcome. The studentship is supported for 3.5 years and
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: https://www.plymouth.ac.uk/schools/psychology/phd-studentships . It is essential that candidates discuss their proposal/plans with their intended supervisor(s) prior to writing their proposal and
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compatibility. Importantly, they will safely degrade after use, reducing medical waste. This interdisciplinary PhD is ideal for candidates interested in biomedical engineering, materials science, or electronics
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are invited for a 3.5-year EPSRC funded UDLA PhD studentship. The studentship will start on 1st October 2026. Project Description Offshore solar energy is an emerging and highly promising technology for
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qualification. Background knowledge and experience in engineering and computer science disciplines and in areas such as CFD post-processing (OpenFOAM), wind turbine renewable energy systems, big data management
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@plymouth.ac.uk) 3rd Supervisor: Prof. Shangming Zhou (shangming.zhou@plymouth.ac.uk ) Industrial Partner: This project is partly funded by Advanced Micro Devices (AMD) . AMD is a leading global semiconductor
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Science backgrounds could also consider applying. Applications from both UK and overseas students are welcome. The studentship is supported for 3.5 years and includes full Home tuition fees, Bench fee plus
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. The studentship will start on 1st October 2026. Project Description Project Aim To develop and validate an AI co-pilot software system integrating multi-modal radiomics data to enhance cancer detection speed and
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during tasks, ageing, or disease. A further key aim is to combine topological features from structural MRI and functional fNIRS to produce robust, interpretable biomarkers that have the potential