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systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural language understanding (to interpret instructions), and action generation (to respond), enabling robots
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programmed in advance. If anything changes, it may fail. This project explores how to build more adaptable systems using vision-language-action (VLA ) models. These combine computer vision (to see), natural
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on combining innovative technologies such as remote monitoring, large language models, machine learning, blockchain, and eco-accounting to enhance the efficiency, security, and sustainability of e-bike charging
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explore multimodal learning and unlearning techniques using vision, language, and audio signals to build intelligent systems capable of interpreting and responding to human actions and emotions. This work
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that lower the carbon and computational footprint of training and inference. Parameter-efficient fine-tuning: Harnessing large foundational vision–language models using adapters, LoRA, low-rank updates, and
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on particle complexity and surface roughness, using computational methods like physical optics and the discrete dipole approximation. Supervisors Entry requirements Applications are invited from individuals
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) advances in imaging techniques that fuel a more detailed understanding of the brain, 2) tools from artificial intelligence that enable building better computer simulations of the brain. The lab will leverage
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flexibilities and request an exemption. Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills. International applicants may require an ATAS
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operators in a cycle of designing bespoke, inflexible models. Large Language Models (LLMs) represent a paradigm shift, offering a path to a more sustainable and intelligent approach. Their emergent
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welcome to apply and if successful will receive a full studentship. Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills