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sits at the intersection of AI Safety and Data-Centric AI. We aim to make large-scale ML more reliable, transparent, and aligned with human values. We are specifically interested in: Data-centric AI
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PhD Studentship: Designing Human-AI Teams for Meaningful Human Control of 'Machine-Speed' Operations
these might change as the research develops along lines that the PhD student and sponsors find particularly compelling): How can we align human decision-making and situation awareness with the processing speed
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/ITS sequencing) to understand how residues drive nutrient cycling and soil health. Parameterise predictive models and translate outputs into practical grower tools, including a fertiliser credit
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fragments (Abaqus cannot model inertia in a coupled temperature-displacement model) and the ability to model the migration of multiple chemical species. The motion of pellet fragments will be of paramount
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theoretical modelling. The project will focus on self-learning active mechanical networks, but will be tailored to align with the interests and expertise of the successful candidate - we will mutually ensure
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this group have evolved systems of non-concatenative morphology in which multiple grammatical categories are expressed simultaneously within a single syllable, through specifications of tone, phonation, vowel
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technical testing and creative practice, focusing on functionality, editability and creative control and agency. The project offers potential alignment with CoSTAR and its research into virtual production and
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the growth, alignment, and assembly of framework materials using external fields (electric, magnetic, or flow) to create structured, anisotropic, and multifunctional materials. By coupling field-driven
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, and more consistent. Current AI systems struggle because they assume a single correct answer, when geological evidence often supports multiple plausible interpretations; so, training AI to copy one
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and seismic datasets to characterise reservoir architecture, heterogeneity and petrophysical properties. Develop geological and petrophysical models that capture subsurface heterogeneity across multiple