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use this formalisation to encode our STV algorithm on encrypted ballots. This approach aims to ensure both the correctness and privacy of the tallying process, paving the way for verifiable and secure
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to receive a first or upper-second class honours degree in Materials Science, Mechanical Engineering, Physics, or a similar discipline. A postgraduate master’s degree is not required but may be an advantage. A
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and will be jointly supervised by: Dr Dominik Leichtle, School of Informatics, University of Edinburgh Dr Elham Kashefi, School of Informatics, University of Edinburgh Dr Ivan Rungger, National Physics
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by the Ada Lovelace Centre and the University of Birmingham. This interdisciplinary project is ideal for candidates with a background in physics, materials science, chemistry, or computational science
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or strong Upper Second-Class degree in Mechanical Engineering, Materials Science, Physics, or a related discipline. A background in computational mechanics, materials modelling, or engineering
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(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by
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: Flexible, likely start February 2026, for 42 months. Candidates must possess or expect to obtain, a 2:1 or first-class degree in Engineering, Chemistry, or related physical sciences related discipline. To
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possess or expect to obtain, a 2:1 or first-class degree in Engineering, Chemistry, or related physical sciences related discipline. To apply: send covering letter, CV and academic transcripts by email to
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an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine learning techniques and experience with coding in Python is beneficial (but not a strong requirement
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inhibitors in preclinical GBM models. Eligibility Applicants should have a first or upper second-class honours degree (or equivalent) in a relevant discipline, such as cell biology, biomedical sciences