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Project Description: This funded Humanities PhD project will be conducted as part of the new Centre for Net Positive Health and Climate Solutions. The project will focus on cultural histories of eco
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No of positions: 1 This 3.5 year PhD project is fully funded at UK rates; the successful candidate will receive a tax free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year. UK students and those with settled status are...
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processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs) into reliable and efficient decisions. The work spans
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management experience across multiple industries, build an extensive professional network spanning academia and industry, and cultivate commercial awareness that enhances your career prospects. Alumni from
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Warfare, Information and Cyber (EWIC) provides an exceptional environment for this research, offering access to specialised expertise, cutting-edge labs, and a network of defence and security partners
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of the student’s project and allow them to network. The partners will also be able to offer the PhD student the opportunity of placements, during which the student can gain major insights. This would help them
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the Internet of Things (IoT), where networked sensors and actuators enable real-time adaptation to environmental changes. Consider a self-adaptive IoT network such as a smart home that autonomously manages
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, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from nonlinear control and optimisation
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engineering, computational neuroscience, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural