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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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Fixed-term: The funds for this post are available for 3 years. A doctoral studentship is available in the forthcoming Aspirational Computing Lab (February 2026) in the Department of Computer Science
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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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) 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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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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processes associated with CIN [1], leveraging single-cell DNA sequencing understand CIN heterogeneity [2], and development and implementation of machine learning and AI models to imaging data [3]. The student
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combines hands-on training, cohort-based learning, and cutting-edge research, preparing graduates for careers in academia, industry, startups, and beyond. We welcome applicants from the Physical Sciences
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Fixed-term: The funds for this post are available for 3.5 years in the first instance. Dive into the future of visual technology with a studentship at the forefront of modern imaging science, where
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that meet scalability, fresh and hardened state, and lower carbon goals, and inform how novel concrete mixes can be adopted and used with confidence more quickly. For project-specific enquiries please e-mail
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for this post are available for 3 years in the first instance. To apply online for this vacancy and to view further information about the role, please click 'Apply' above. For any enquiries about the project