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Justin Sheffield. This project aims to transform our understanding of soil moisture (SM) variability and its interactions with land-atmosphere processes. The project will use cutting-edge modelling, data
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remission as a model of flare vs remission with a 50% relapse rate. Initial data analysis has taken place using the 10x Chromium single cell sequencing identifying putative cellular subpopulations
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written and verbal communication skills, experience with developing and implementing Bayesian statistical models, and be proficient in computer programming in e.g. R or Python, and C/C++. Please ensure you
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vitro cellular assays. Experience in using mouse disease models is also desirable. Scientific knowledge in the areas described above equivalent to PhD* level is required. The post-holder will possess
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set-up, and data collection and analysis. - Have the ability to analyse and interpret data using appropriate statistical packages (e.g., conducting linear mixed effects models in R). - Have experience
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UKESM1 or similar models, advanced data analysis and machine learning, would be advantageous. Grade E: You will be near completion of a relevant PhD or have equivalent research experience, and be able
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) facilities and providing support on experimental test campaigns. This will include assisting with tunnel and model instrumentation, data post-processing, analysis and management for experimental projects
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provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute
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set-up, and data collection and analysis. Have the ability to analyse and interpret data using appropriate statistical packages (e.g., conducting linear mixed effects models in R). Have experience
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provide guidance to MSc and PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Person