751 phd-studenship-in-computer-vision-and-machine-learning PhD positions in United Kingdom
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Recently, many councils in Wales started using single transferable vote (STV) method for counting ballots. While counting plaintext ballots using STV method is straight forward, but a rank-based ballot may leak the voter’s preferences if published publicly in plaintext, as the ranking order can...
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. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of
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or computational modelling is desirable but not essential; applicants should demonstrate strong academic performance, resilience in the face of research challenges, a proactive attitude to learning and a willingness
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We invite applications for a fully funded PhD studentship jointly hosted by the University of Birmingham and Diamond Light Source, the UK’s national synchrotron facility. This position is co-funded
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Fees for 4 years (Full time), £12,000 stipend for 3 years We are pleased to invite applications for a funded PhD studentship exploring how a curated Arts Public Programme can address pressing issues
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Supervisory Team: Leonardo Aniello, Han Wu PhD Supervisor: Leonardo Aniello Project description: Blockchain and Federated Learning (FL) are two emerging technologies that, when combined, offer a
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which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng and Dr Tianjin Huang (Machine Learning), Prof Yalin Zheng (AI in Healthcare), A/Prof
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start dates: 1 October 2025 (Enrolment open from mid-September) Supervisors: Hari Arora (Biomedical Engineering), Richard Johnston (Materials) and Iain Whitaker (Medicine) Aligned programme of study: PhD
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A fully funded 3.5 years PhD position in the field of experimental nanoscience at surfaces and molecular imaging of conjugated polymers is available in the group of Prof. Giovanni Costantini at UoB
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust