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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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imaging features to phenometabolic signatures from tissue samples taken during surgery and matched to the imaging. The project is part of the imaging theme for the new Nottingham Brain Tumour Research
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to phenometabolic signatures from tissue samples taken during surgery and matched to the imaging. The project is part of the imaging theme for the new Nottingham Brain Tumour Research Centre of Excellence. This is a
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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