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endeavour is to leverage Large Language Models (LLMs) in a language-vision framework to mitigate the risk of AF utilising ECG image data digitised from paper records in conjunction with relevant clinical data
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Alexopoulou. The PhD student will work with Dr Alexopoulou as a member of the EF Research Lab for Applied Language Learning. You will join the research cluster for Language Acquisition and Language Processing
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Applications are invited for a three-year PhD Studentship based in the Section of Theoretical and Applied Linguistics at the University of Cambridge. This studentship, starting on 1 October 2024, is
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the second year and some conference attendance in the second and third years. Overview Forming an independent project in its own right, this PhD studentship in applied linguistics orFrench (applied
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English language requirements. provide evidence of adequate oral and written communication skills. meet RMIT’s entry requirements for the PhD by research degree. Interested candidates should contact Dr. Ganesh
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Mathematical and numerical models of piezoelectric wave energy converters (MPhil or PhD scholarship)
undergraduate degree or equivalent, in applied mathematics, engineering, or a related discipline have experience writing numerical software in a relevant language (e.g., MATLAB, Julia, Python or C) and modelling
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Vacancies PhD position: Can Clinical Language Processing Improve Decisions? Key takeaways Key takeaways The PhD project will explore if and how Natural Language Processing and Advanced Statistical
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learning of generative protein language models. The successful candidate will have the unique opportunity to work at the interface of computational and experimental biology, implementing and refining machine
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Supervisory Team: Dr. Muhammad Burhan Hafez (Lead Supervisor), Professor Adam Sobey PhD Supervisor: Muhammad Burhan Hafez Project description: The integration of Large Language Models (LLMs
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excitation. This will create new insights into how these materials can be utilised for novel applications. Supervisor Bio Dr Matthew Ellis’ research intersects machine learning and physics; looking to better