41 software-defined-network-phd PhD positions at University of Exeter in United Kingdom
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About the ProjectProject details: Next-generation networks are rapidly outscaling the capabilities of traditional management paradigms. While early AI/ML models offered a degree of automation, they
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networks, which could be used to support the transition to a sustainable, low-carbon energy future. The studentship will be awarded on the basis of merit. Students who pay international tuition fees
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Change, University of Exeter The University of Exeter invites applications for a PhD studentship in geospatial ecology starting from April 2026 onwards. The student will join the Terrestrial Ecosystem
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Researcher. The PhD researcher will gain expertise in implementing RL and Evolutionary Optimisation to a real-world usecase on dynamic crowd and network management provided by the industrial partner INOCESS
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software development, and time management. The candidate will regularly visit the MetOffice for scientific exchanges and data acquisition. Useful recruitment links: For information relating to the research
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
generation of wireless communication (6G) to extend network coverage, supporting diverse data-intensive applications such as immersive extended reality and autonomous systems. However, aerial 6G networks will
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this project have direct applicability to UK commercial forestry and Net Zero ambitions due to increasing tree planting programs coupled with increased extreme weather events. Please direct project specific
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climate models, including the UK Earth System Model (UKESM), resulting in critical gaps in both seasonal forecasts and long-term climate projections. This PhD will develop a new parameterisation of snow
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to other indicators of unrest, such as seismicity. This PhD project will drive innovation in modelling magma-mush processes and the generated surface deformation and seismicity during unrest episodes
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behaviour in a practical, real-time monitoring system requires advances in both sensor engineering and behavioural data interpretation. This PhD project aims to develop a next generation environmental