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This PhD opportunity at Cranfield University invites ambitious candidates to explore the frontier of energy-efficient intelligent systems by embedding AI into low-power, long-life hardware platforms
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This PhD opportunity at Cranfield University invites candidates to pioneer research in embedding AI into electronic hardware to enhance security and trustworthiness in safety-critical systems
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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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A Design Methodology for Embedding Robotics & Automation into Circular Product Development This is an exciting opportunity to undertake industrially linked research in partnership with
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have the opportunity to work with real-world cyber-physical systems (electric vehicles, space and satellite systems, and power grid testbeds), and will develop expertise in embedded systems, wireless
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, embedded systems, advanced implant probe design and signal processing. There are opportunities for students from a variety of backgrounds to work on the following possible projects: Bionic Vision
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control engineering and/or numerical optimisation Interest in embedded systems or real-time autonomous applications Programming experience in a compiled language (e.g. C, C++, Rust) Application Procedure
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Project advert Video-based technologies can capture players’ match kinematic data. Recently, FIFA have implemented a video-based skeletal tracking system into their referee decision support
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Project details: Global Navigation Satellite Systems (GNSS) support a wide range of applications, from smartphone navigation to autonomous vehicles. The global GNSS market is substantial, with
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Science), a global leader in marine science, the project will develop scalable, low-cost embedded vision systems to analyze marine biodiversity and detect anthropogenic debris. The core challenge is