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Course: PhD studentship Project title: Exploring a One Welfare approach to human-animal-computer interaction in digital dairy technologies Principal supervisor: Charlotte Doidge Other supervisors
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human-animal-computer interaction lens, examining how digital tools shape farmer-cow relationships, perceptions of care, and welfare‑related decisions. The project will investigate how farmers engage with
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of complex behaviours such as void formation and brittleness. In parallel, you will explore formulation and processing strategies to improve toughness, reduce embodied energy and eliminate the need for cold
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develop a digital twin of the PFA cure process, combining mechanistic modelling with neural‑network‑based prediction of complex behaviours such as void formation and brittleness. In parallel, you will
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. To minimise material and energy wastage, digital models of the manufacturing processes can be developed and linked to process control and optimisation. State-of-the-art digital models and AI tools
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regeneration, resulting in significant energy penalties and limited operational flexibility. This project proposes a novel biomass-based negative emissions process that leverages the reversible CaO/CaCO
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The Computer Vision Group is looking for an aspiring PhD to investigate multi-agentic AI, LLMs, and VLMs applied to agricultural sciences. Currently, established AI models often fail to generalize
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), produce a flue gas with a high concentration of CO2 that allows easier sequestration without energy-intensive preprocessing. However, production of oxygen is an energy-intensive process. Industrial scale
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recognised as a critical pathway for net-negative emissions but current systems are energy-intensive and inflexible. This project proposes a novel biomass-based negative emissions process using the reversible
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approach towards artificial intelligence that uses the natural dynamic behaviour of physical systems (such as light and electronics) to process information efficiently. You will work at the intersection