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The EPSRC Centre for Doctoral Training in Quantum Informatics (QI CDT) invites applications for up to 16 fully funded four-year PhD studentships in Quantum Informatics with Integrated Study for
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2D materials, searching for exotic quantum functionalities to form new sustainable electronics and new types of computing. Tuning nanostructures of these materials with extreme pressure will unlock
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Research Studentship in Quantum Computation 3.5-year D.Phil. studentship Project: Controlling Errors in Quantum Computers Supervisors: DrZhenyu Cai, Prof. Dominic O’Brien This DPhil project will
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Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project costs will also be provided Overview Edge artificial intelligence (Edge AI) enables deploying AI algorithms and models directly on edge devices. However, AI...
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become accessible experimentally only about twenty years ago, and is now used to realise the first noisy quantum computers. In particular, the main goals are to understand: the non-equilibrium dynamics
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, Computer Science, Natural Sciences, Quantum technologies, although we are open to any enthusiastic applicant with a science-related background. The start date of the studentship is October 2026 (or earlier
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, Computer Science or a closely related field, ideally with a strong focus on quantum computing and numerical simulations. Experience in data-reuploading quantum algorithms, optical quantum computing and quantum
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Department: School of Computer Science Details of Studentship: Fully Funded PhD Studentships Applications are invited from Home and International students for a number of fully-funded PhD
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supervised by: Dr Joschka Roffe, School of Informatics, University of Edinburgh Dr Stergios Koutsioumpas, School of Informatics, University of Edinburgh Dr Vivek Chidambaram, National Quantum Computing Centre
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep