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towards this goal. The PhD research fellow will be part of the PhD programme in Computer Science: Software Engineering, Sensor Networks and Engineering Computing (https://www.hvl.no/en/research/phd
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Electronic Health Records from people with epilepsy across multiple NHS hospitals in England and Wales. They are expected to have some experience working with NLP in general and LLMs in particular. Familiarity
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Electronic Health Records from people with epilepsy across multiple NHS hospitals in England and Wales. They are expected to have some experience working with NLP in general and LLMs in particular. Familiarity
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Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of Electronic Health Records from people with epilepsy across multiple NHS
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with epilepsy across multiple NHS hospitals. They are expected to have some experience working with NLP in general and LLMs in particular. They will also help to further develop machine learning models
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novel scientific computing, physics-informed machine learning models including neural ODEs, neural operators and time-series models for Li-ion batteries. Create data and AI-based optimization methods
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generative Large Language Models (LLMs), to interrogate a very large sample of Electronic Health Records from people with epilepsy across multiple NHS hospitals. They are expected to have some experience
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entitled “White Matter Computation: Utilising axonal delays to sculpt network attractors”. The central aim of the project is to determine how dynamic patterns of neural activity evolve in a complex network
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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. This position is specifically targeted to candidates interested in computation in recurrent neural networks and is funded for multiple years by the NIH. Responsibilities: Successful candidates will conduct