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lab has developed the OrthoFinder comparative genomic methods. OrthoFinder has become widely-used in comparative genomics research, it powers many popular databases of online genomic information, and
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becomes essential. This project will focus on building a comprehensive digital twin of a future quantum computer to investigate how classical subsystems scale and interact, and how this scaling impacts
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follow up and recruitment of the children, at age 1 to 4 years, scheduling and conducting screening and study assessments, performing ultrasound imaging, and managing data entry and quality control. As the
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at command line and BASH scripting Experience working with large scale, complex datasets and data wrangling skills Strong publication record and familiarity with the existing literature and research in
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must hold, or be close to completion of a doctoral degree in a relevant field (e.g., data science, geography, environmental science, public health, economics). You will have experience relevant for food
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data
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especially suitable for someone with strong formal reasoning and data analysis skills who is considering progression to a PhD or further postdoctoral research in AI ethics, social choice theory
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on modelling of the transport, electrochemistry, and mechanics of next-generation lithium/air electrode materials and cell architectures. Using input data from experiments, the post holder will develop and
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interpretation of atmospheric circulation in high-resolution reanalysis data, idealised model simulations and a state-of-the-art weather forecasting system. The post-holder will have the opportunity to teach
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working with families, professionals and teachers to best support neurodivergent children and young people in different settings. You will lead data collection using quantitative and qualitative methods