25 phd-studenship-in-computer-vision-and-machine-learning PhD positions at University of Cambridge
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) data. We also analyse macaque electrophysiology data obtained through collaborations. We use machine learning techniques for data analysis and computational modelling with a special interest in
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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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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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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose
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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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Department/Location: Yusuf Hamied Department of Chemistry Applications are invited for a 4-year PhD studentship based in the Department of Chemistry, University of Cambridge and the new AstraZeneca
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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