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A position exists, for a Research Assistant/Associate in the Department of Engineering, to work on research in the fields of machine learning, hydrology, and water resources management at various
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climate, sociodemographic and hydrological variables. The system is currently operational and the postholder will primarily focus on generating higher resolution forecasts, improving how forecasts inform
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respond under future climate scenarios in terms of their net carbon balance and other factors. Examine approaches to rehabilitate peatlands, ecologically (plant macros), and hydrologically (testate amoebae
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the complex interplay of mechanical, ecological, and hydrological processes in natural porous media, and to increase our understanding of the dynamics of Peatland, and carbon sequestration within it. Peatlands
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for the coupled model and with the hydrological modellers who will use glacier coverage and runoff as inputs You will join a team of investigators in the Schools of GeoScience in the University of Edinburgh, with
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) at the University of Leeds and will be a collaborative enterprise between the Schools of Mathematics, Computing and Earth & Environment in Leeds, and the UK Centre for Ecology and Hydrology (UKCEH) in Wallingford
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for Ecology and Hydrology (UKCEH). The resulting tool will be integrated into the E-Surveyor application developed by UKCEH. As part of this exciting opportunity, you will be responsible for the development
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for Ecology & Hydrology. This PDRA post will work across different disciplines and develop novel mathematical models and tools to uncover when, where and how phenotypic plasticity shapes species' responses
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physical geography, geomorphology, hydrology or equivalent Possess strong GIS, large dataset manipulation and coding skills, preferably using Python or R Experience of applying machine learning and/or
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of mechanical, ecological, and hydrological processes in natural porous media, and to increase our understanding of the dynamics of Peatland, and carbon sequestration within it. Peatlands store the most carbon