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-set. You will be funded for 3 years and will pursue a doctoral degree during this time. This DR position is based at the University of Leeds and will involve expanding and developing our iSIM (instant
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an appropriate field of Engineering. Exceptional candidates with a First Class Bachelor’s Degree in an appropriate field will also be considered. Subject Area Artificial Intelligence, Computer Vision
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provide a route to topological quantum computing by exploiting the presence of Majorana bound states. Please state your entry requirements plus any necessary or desired background First or Upper Second
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Wladislaw Michailow (University of Cambridge) The possibility of developing quantum computing machines that can break the widely used public key cryptosystems has required us to at new solutions for scenarios
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generation, particularly in high-chloride aqueous environments; a vital yet understudied aspect relevant to the UK’s GDF program. This research programme employs short-term electrochemical and long-term
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, based on a population balance model, will be developed for the prediction of crystal size distribution, which will be integrated with a Computational Fluid Dynamics (CFD) software (e.g. ANSYS Fluent
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background Candidates will have, or be due to obtain, a Master’s Degree or equivalent from a reputable university in an appropriate field of Engineering. Exceptional candidates with a First Class Bachelor’s
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@leeds.ac.uk Rapid technological progress has ushered in the era of quantum computers and, with them, the race for quantum advantage. However, identifying problems for which quantum devices can show true
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Funding A highly competitive EPSRC Quantum Technologies Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years. Training and support will also be provided. Lead Supervisor’s full name & email address Dr...
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the Schools of Chemistry and Chemical Engineering at the University of Leeds, will investigate flow chemistry and machine learning approaches for the development of an automated reaction screening platform