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training and fine-tuning large language models (LLMs) to extract structured clinical concepts from unstructured EHR, pathology, and radiology reports. The role involves publishing in high-impact, peer
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model checkers; proofs of safety and/or security properties; programming languages and/or type systems; concurrent and/or distributed algorithms; and related topics. The successful applicant will work in
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training and fine-tuning large language models (LLMs) to extract structured clinical concepts from unstructured EHR, pathology, and radiology reports. The role involves publishing in high-impact, peer
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the knowledge of: 1) deep learning, large AI models, large language models; 2) the rendering techniques for generating human body animations from 3D joint coordinates; 2) understanding wireless transmission
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, in a relevant topic and relevant experience with mathematical modelling of infectious diseases. Strong knowledge of a programming language (e.g. R, Python) is essential. Experience in mathematical
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, in a relevant topic and relevant experience with mathematical modelling of infectious diseases. Strong knowledge of a programming language (e.g. R, Python) is essential. Experience in mathematical
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to work collaboratively. They should have good communication and team-working skills, and a strong command of English. You will join a dynamic group of academics, postdocs and PhD students using in situ
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to the advancement of AI applications in biological sciences. This role presents a unique opportunity to work with pangenomic datasets while exploring the application of Large Language Models (LLMs) and machine
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. Candidates must also have excellent programming skills in a relevant scientific language, such as Mathematica or Python, to facilitate advanced data analysis of measurement outputs. Apart from being able
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of epidemiological modelling and data analysis C2 Up-to-date knowledge of epidemiological modelling approaches C3 Knowledge of relevant programming languages for model development, for example Java, R, C++, Julia C4