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PhD studentship in Trustworthy Multimodal AI under Lightweight and Data-Efficient Architectures Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26
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breaking towards achieving silent aircraft design? Applications are invited for one funded 3.5-year PhD studentship for the project titled ”Data-driven modelling and control of turbulence-generated noise” in
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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In this PhD you will help explore a new trajectory for AI research, EVE - everyone virtuoso everyday - to succinctly summarise the drive of the work. That is, we are interested in defining and evaluating a class of AI technology that enable expressive, individual and masterful interactions, like...
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mapping, to rapidly generate linked property-microstructure-chemistry multi-modal datasets, to efficiently produce low‑to‑medium fidelity data streams, which can also be paired to high‑fidelity industrial
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overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
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within livestock environments contains a wealth of information about animal behaviour, health and emotional state, yet it remains relatively under-utilised in commercial dairy systems. This PhD will
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the university and department where they will pursue their degree. Applicants should also arrange for two academic references to be submitted. Please use the following links for the Application Form and the
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challenges, with increasing interest in dietary strategies as accessible, low-risk interventions. Flavonoids—naturally occurring compounds in plant-based foods—have been linked to improved cognitive
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access to amenities, active travel opportunities, and health-promoting environments. This project will empirically test both assumptions to develop a more nuanced, evidence-based framework linking chrono