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for your preferred PhD programme must be submitted by 13 January 2025. You must submit your research council funding application by the given deadlines: AHRC North West Consortium Doctoral Training
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Applications are invited to undertake a 3.5 year PhD programme to explore new laser surface engineering methods for functionalising the electrochemical performance of solid-state batteries. In
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application for your preferred PhD programme must be submitted by 13 January 2025. You must submit your research council funding application by the given deadlines: AHRC North West Consortium Doctoral
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PhD Studentship: Open Radio Access Network (ORAN) for Distributed Edge Computing Orchestration in 6G
Overview: Self-funded PhD opportunity in 6G as part of major project hub, further funding possible subject to progress of project and student. Focus on native AI in 6G systems with experimental
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Applications are sought for a fully-funded 42 month PhD studentship to work with Dr Rachel Nicks and Prof Stephen Coombes on the project: White Matter Computation: Utilising axonal delays to sculpt
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multimodal machine learning, large language models, and fairness and uncertainty evaluations. The PhD student will benefit from: State-of-the-art AI computing recourses for large-scale model training including
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email to start the application process. During the application process please select the PhD in Classics programme. PhDs can be undertaken full-time or part-time, but not By Distance. *Important notes
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desirable. If your first language is not English, you will need to meet the minimum English requirements for the programme, IELTS Academic score of 6.5 (with no less than 5.5 in each component test area) or
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research training element would be highly advantageous. If your first language is not English you need an overall IELTS score of 6.5 (at least 5.5 in all sub-skills) or equivalent language qualification
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algorithms, have excelled in tasks like computer vision, image recognition and large language models (LLM). However, their reliance on extensive computational resources results in excessively high energy