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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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at international conferences and build a professional network across academia and industry. Development of expertise in cutting-edge experimental techniques, computational modelling, and interdisciplinary
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, University of London in August 2024. As a PhD candidate, you'll become an integral part of the School of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading
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Families, and sponsors of International Women in Engineering Day. We are also Disability Confident Level 1 Employers and members of the Business Disability Forum and Stonewall University Champions Programme
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The Intelli-Ingest Doctoral Network is an EU-funded Marie Skłodowska-Curie Action (MSCA) initiative is a network bringing together leading academic, clinical, and industrial partners to train a new
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for these architectures. This will also guide hardware design for such devices and tackle crucial challenges in networked systems and entanglement transmission. Candidate’s profile Knowledge of quantum computing and an
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seminars and events, alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking
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of Science and Technology (proud member of the Alan Turing University Network) and be supervised by leading experts in machine learning for healthcare. You will also be affiliated to the School of Health
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honours degree in materials science, physics, engineering, or a related discipline. The ideal candidate will be self-motivated, with an interest in both computational modelling and practical manufacturing
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control laws into Trent gas turbine engines and developed algorithms monitoring fleets of 100s of engines flying all around the world. During the PhD, you will have the opportunity to deeply engage with