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and advanced signal processing to detect cyber threats in real-time while minimizing energy consumption. Digital twins, as virtual replicas of physical systems, enable continuous monitoring and anomaly
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the areas of robotics, or autonomous systems, interested in autonomous systems and signal processing. Keen to work with equipment and embedded platforms. Funding To be eligible for this funding, applicants
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integrate, adapt and develop methods (using packages such as BioSPPy ) for the collection, processing and analysis of physiological signals measured for determining deception. The project can take different
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PhD Studentship: Multi-robot Cyber-physical Systems for Solar Farm Health Monitoring and Maintenance
-physical Systems for Solar Farm Health Monitoring and Maintenance Supervisors:Dr Euan McGookin & Dr Ahmad Taha Year 1 MSc Course: MSc Communication and Signal Processing Year 2 – 4 PhD Location: Glasgow
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Supervisors:Dr Kevin Worrall & Dr Yao Sun Year 1 MSc Course: MSc Communication and Signal Processing Year 2 – 4 PhD Location: Glasgow University Research Abstract: This project aims to enhance satellite
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Supervisors:Dr Jianglin Lan & Dr Ahmed Zoha Year 1 MSc Course: MSc Communication and Signal Processing Year 2 – 4 PhD Location: Glasgow University Research Abstract: This PhD research aims to develop an AI-driven
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Supervisors:Dr Dezong Zhao & Prof Lei Zhang Year 1 MSc Course: MSc Communication and Signal Processing Year 2 – 4 PhD Location: Glasgow University Research Abstract: This research aims to enable robots to tackle
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to study the role of these receptors in reward signalling. Application process: Please contact hls-applications@brookes.ac.uk for details of how to apply via the above ‘Apply’ button. Director of Studies
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Research Institute (EPRI), USA, and Forschungszentrum Jülich, Germany. Background: Tungsten (W) is the leading candidate for plasma-facing components in future fusion reactors due to its high melting point
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processes. Carbonate biomineralisation is a key process in global carbon cycling, but there are major gaps in our understanding of how biominerals form. We lack a quantitative understanding that can predict