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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of
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WMG collaboration with a large vehicle manufacturer. About You You will need a good honours degree and a PhD (or be close to submission thereof) in a relevant subject, and experience of the theory and
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of data on invasions, their impacts and their management. The scientist will also analyse data to quantify the costs and benefits of the use of nonnative trees in forestry. They will use these analyses
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. Demonstrated experience in managing large-scale data sets and conducting health-related research studies. Strong analytical skills with proficiency in statistical software and data analysis tools, particularly
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hydrogen/methane blends in 15 MW boilers and prepare a report (thermal power and heat transfer analysis at large-scale); b) Collaborate with UBC technicians in acquiring data from our 15 MW boilers, analyze
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electrolysers or related fields. • Experience with large-scale electrochemical systems, including component selection, system integration, and process scale-up is highly desirable. • Proficiency in
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at improving the quality, safety, and effectiveness of medication use across the reproductive lifespan. The role involves managing and analysing data from clinical trials, cohort studies, and large linked
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, cybersecurity, computer networking, machine learning, AI, ERP and cloud computing Conduct research with colleagues. Strong data analytics skills, including the ability to work with large sets of structured and
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) GRADE 7, £39,355 - £45,413 pa Fixed-term Ref: 095593 A Research Fellow position is available in the group of Professor M. J. Rosseinsky OBE FRS to work in a team of computer scientists and materials
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large amounts of complex data. They should provide evidence of potential for research and publication at the highest level, as well as a strong interest in engagement in empirically-grounded, quantitative