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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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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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) 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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This is a four-year (1+3 MRes/PhD) studentship funded through the Cambridge EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Unlocking Net Zero (FIBE3 CDT). Further
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statement that shows evidence of engagement with this advert. Further information on the PhD in Computer Science programme can be found at: https://www.cst.cam.ac.uk/admissions/phd All applications should be
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for complex data accessible to the scientific community and to produce innovative methodology related to trial designs, longitudinal and event history data, precision medicine, causal inference, AI/machine
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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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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 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 diverse