22 phd-in-computer-vision-and-machine-learning PhD positions at University of Cambridge
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Unit at the University of Cambridge provides an ideal place to commence a career in Biostatistics. Our thriving PhD programme is hugely successful and competition for places is high. The BSU's relaxed
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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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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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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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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 PhD studentship will be based at the University of Cambridge in the Department of Materials Science and Metallurgy as part of the Structural Materials Group. The Structural Materials Group is a
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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