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offering one funded PhD position investigating how LLMs represent and process linguistic knowledge, drawing on tools from computer science, linguistics, and neuroscience. The aim is to build new
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skills. Experience of working independently as well as part of a team How to apply: If you have already been accepted to start your PhD in Computation, Cognition and Language with Prof Anna Korhonen as
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the development of globally inclusive language technologies and to design transformative approaches to overcome them. Responsibilities of the post holders include the development of new methods for multilingual
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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Funding: School of Computer Science studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email
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degree which includes a substantial research element, with a score of 65% or above in the taught modules and 65% or above in the dissertation. Complete the PhD Programme online application . Your
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degree which includes a substantial research element, with a score of 65% or above in the taught modules and 65% or above in the dissertation. Complete the PhD Programme online application . Your
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The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
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language, you'll need an IELTS score of 6.5 or equivalent with no individual element below 5.5. How to apply: Applicants must register on SGSSS Apply, completing their Equal Opportunities data. Applicants
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty