35 phd-studenship-in-computer-vision-and-machine-learning PhD positions at University of Cambridge
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both sites. The project sits at the interface of cell line engineering, protein science and machine learning and you will receive advanced training in these areas while developing methods to accelerate
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Science and Technology (CST) at the University of Cambridge. The goal of this PhD programme is to launch one "deceptive by design" project that combines the perspectives of human-computer interaction (HCI) and
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Research Infrastructure? No Offer Description Two fully-funded 3-year PhD studentships are available in Neuromorphic and Bio-inspired computing at the interface between control engineering, electrical
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Two fully-funded 3-year PhD studentships are available in Neuromorphic and Bio-inspired computing at the interface between control engineering, electrical engineering, computational neuroscience
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considered. Qualifications/Skills PhD degree in a programme relevant to human-computer interaction and/or critical computing, ideally in Computer Science, Industrial Engineering, Interaction Design, or a
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will be tasked with the development of new models for the early detection of CIN cancers, applying bleeding edge computational methods and machine learning approaches to improve detection and
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their research findings. Training in all the computational and experimental methods will be provided in the project, although relevant previous experience would be advantageous. Applications for this PhD project
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the computational and experimental methods will be provided in the projects, although relevant previous experience would be advantageous. Applications for this PhD project should be submitted via the University
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Location: West Cambridge Funder: Tata Steel and the University of Cambridge Duration: 4 years from 1 October 2026 Supervisors: Prof Howard Stone and Dr David Collins Location: The PhD studentship
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The Centre for Doctoral Training in Nanoscience and Nanotechnology (NanoDTC) at the University of Cambridge invites applications for its 3.5-year interdisciplinary PhD programme. The programme