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motivated PhD student to join our interdisciplinary team to help address critical challenges in high-speed electrical machine design for electrified transportation and power generation. Together, we will make
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Partnership at the University of Cambridge. This programme is a collaboration between industry and academia, now in the final 24 months of a 60-month effort. The programme integrates research and innovation
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collaborate with Tokamak Energy to distil key experiences to ensure a scaled system design for a commercial fusion power plant. Key aspects of impurity management, continuous operation and modelling
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mechanisms, with the current generation having significant drawbacks, including low energy efficiency, high operating voltage or temperature. This project will develop the materials, methods, and designs
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challenges in high-speed electrical machine design for electrified transportation and power generation. Together, we will make technological advances that support the global transition toward net-zero
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PhD Studentship: Sleep and Circadian Rhythms in Elite Sport (Co-funded by Brighton & Hove Albion FC)
state-of-the-art wearable devices. Applicants should have strong experience in data collection in applied settings, advanced statistical analysis skills (particularly in time-series data), and proficiency
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Fully Funded PhD Research Studentship tax-free stipend of £20,870 Design, Informatics and Business Fully Funded PhD Research Studentship Project Title: Autonomous AI-powered red agents for enhanced
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-temperature resistance, dimensional stability, or chemical compatibility. However, current research into circular design and end-of-life strategies often overlooks the unique recovery and regeneration challenges posed by
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(coordination) and safety constraints can be intractable. Your work will bridge this gap by providing generalizable, provable design approach that apply across a wide range of networked systems. This 3.5-year PhD
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collaborative project that spans multiple continents. You will contribute to the development of new chronobiological analytics on existing data, design experiments to collect novel chronobiological data, engage