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-developing the principles (e.g. ethics) and methods for anonymising, processing and analysing sensitive data collected by a national charity’s 24/7 helpline for people experiencing or witnessing elder abuse
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. For instance, LLMs are typically trained on vast and diverse datasets, many of which contain personal information about thousands or even millions of individuals; the large repositories of data used to train and
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modelling, quantification of uncertainties and data analysis. They will work closely with a large delivery team at the UK Centre for Ecology & Hydrology, British Geological Survey and Imperial College London
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create a working framework that includes both experimental and modelling prototypes, including AI/ML tools to assist with the large number of variables involved. This project is seeking candidates with a
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they are no longer eligible for the SFVS to see if things change. Quantitative and qualitative data will be collected through interviews and focus groups. A Research Fellow will also be appointed at Northumbria
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defending the cultural value of knowledge for its own sake. You will also possess computational expertise in data mining and / or analysis, ideally including language processing, and be able to work with an
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Medicine Hub for Applied Bioinformatics). We are looking for an ambitious candidate with established expertise in bioinformatics, specifically dealing with large data sets and data integration. Knowledge
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funded large projekt, requiring relevant academic experience in the field of pharmaceutical sciences or related disciplines (drug discovery) and preferably experience in projekt management. Your future
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Research Fellow in Intervention Development to join the Big Data in Health Grou About us Our big data in health team at the University of Southampton is based in the Primary Care Research Centre. We
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engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early