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for downstream tasks. In this project, you will develop novel unsupervised machine learning methods to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a
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structures, and to examine and model the ‘ageing’ effects that take place during long-term storage for space modules that may then be subjected to the space and de-orbit environment. Key tasks will be
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an independent impact assessment of potential climate interventions in the Arctic marine environment through laboratory experiments and computer modelling. The team will develop physical, climate and ecosystem
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sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data
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Partnership at the University of Cambridge. This programme is a collaboration between industry and academia, now in the final 24 months of a 60-month effort. The programme integrates research and innovation
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performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with ultrasonic sensors for real-time seal gap measurement. Combine experimental research and mathematical modelling
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November 2025 or as soon as possible thereafter. This PhD project aims to explore how emerging datasets could provide value to the UK’s insurance industry through a combination of data analytics, modelling
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position will contribute to the research programme of the recently founded "AI Hub in Generative Models", a research consortium funded by EPSRC. The goal of the programme is to do research in the area of
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environmental impacts of digital activities. You will lead projects modelling the energy usage of different computing equipment (personal computers, servers, High-Performance Computing infrastructure
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focus on solid dosage forms and 3D printed drug products. The research will integrate advanced imaging, computational modelling, and pharmaceutical sciences to improve the resolution, reproducibility, and