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-carbon electric power systems, taking into account wake interactions between individual wind turbines. The project focus is on how to generate and utilize reduced-complexity predictive models for windfarm
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the information encoded in our genome to better diagnose, treat, predict and prevent disease. From the individual patient with rare disease, to the many thousands affected by complex, widespread illness, we
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) optimisation of a multi-component model of care for bipolar disorder, b) evaluating the use of digital data to improve phenotyping and prediction of mood disorders and c) developing tailored treatments for mood
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roots. Research will lead to an improved capacity to predict soil organic matter dynamics in grassland ecosystems affected by drought. The role presents an opportunity to collaborate with leading partners
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pressure, temperature, mineralogy and brine composition. The output of the research includes an extended database predicting relative phase permeability, capillary pressure and kinetics coefficients
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carbon using proximal ground or satellite-based sensors Technology Innovation: Research and develop of infrared technology applications to predict and analyze soil carbon efficiently, enhancing our
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to create and test a predictive model of community dynamics and function. The postdoctoral position is centered on microbial ecology, but the ability to combine empirical results with computational biology
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Analysis: Conduct hands-on research to understand corrosion mechanisms affecting asset performance, nd apply modelling techniques to predict and mitigate their impact Collaboration: Work closely with a
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of the research includes an extended database predicting relative phase permeability, capillary pressure and kinetics coefficients for geochemical reactions in the target geological formations. Another output will