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The School of Computer Science at the University of Nottingham is pleased to invite applications for a fully funded PhD studentship in deployable, efficient, and trustworthy computer vision. This is
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Exciting Fully Funded PhD: Computational Modelling for High-Pressure, Low-Carbon Storage Technologies. Be a Key Player in Shaping the Future of Clean Energy Storage! School of Mechanical, Aerospace
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PhD studentship in Computer Science - Dynamic Validation of AI Systems in Digital Twins: A Real-Time Safety Framework for Critical Infrastructure Resilience Award Summary 100% fees covered, and a
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accountably. This is where you come in. Your mission Working at the intersection of privacy research and real-world engineering, you'll design and build a computational stewardship engine—an autonomous agent
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Imagine a world where sensors don’t just collect data, they think. With the exponential growth of the Internet of Things (IoT) and edge computing, the demand for ultra-low-power systems that can
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administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate: 01.02.2026 | Working hours
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of future generations. This Leverhulme doctoral programme will train a new generation of interdisciplinary scholars to develop a deeper, holistic view of the interactions between humans and freshwaters
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In this PhD you will help explore a new trajectory for AI research, EVE - everyone virtuoso everyday - to succinctly summarise the drive of the work. That is, we are interested in defining and evaluating a class of AI technology that enable expressive, individual and masterful interactions, like...
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proposing an ambitious programme of impactful research, teaching and training with geographical foci in Africa, India and the Arctic Nations. This studentship will make an important contribution
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before the deadline. Computational haemodynamic modelling provides a powerful framework for linking blood flow dynamics with cardiovascular disease, using in silico approaches to systematically study flow