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specialist knowledge in a relevant subject area. With knowledge of statistics and ability to use statistical packages for analysing data, you will have excellent communication skills and the ability to work co
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Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the
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the risks. You will have: a PhD in one of the relevant STEM disciplines, such as mathematics, statistics, computer sciences, theoretical food, ecological or physical sciences, etc. skills in mathematical
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analysis of data from a Nipah virus vaccine trial, using machine learning and statistical tools to identify immune response markers for future trials. You will be responsible for developing new and adapting
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, computer science, statistics, or a related field together with strong programming skills in Python, R, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale
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/algebraic statistics in the School of Mathematics at the University of Edinburgh. The post is available from 1st September for a 36 months fixed-term, full-time contract with a potential 6 month extension
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available option. Applicants with a range of academic subject backgrounds are welcomed, including natural sciences, engineering, statistics and applied mathematics with experience and/or growing interest in
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. Proficiency in the use of statistical programming languages and analysis of large datasets and strong publication records would be essential. Previous experience in atmospheric dynamics and predictability is
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Agency (ARIA). The PROTECT project (Probabilistic Forecasting of Climate Tipping Points) brings together cutting-edge AI, statistical, and machine learning techniques with climate modelling, aiming
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design, statistical sampling and analysis of large, multi-taxa biodiversity datasets. Expertise in landscape-level biodiversity and production analyses using R, QGIS, Google Earth Engine. Extensive