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Field
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: Data analysis - activities: data analysis, statistical analysis, spatial datawet analysis Where to apply E-mail Crystele.leauthaud@cirad.fr Requirements Research FieldAgricultural sciencesEducation
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hydro-climate studies. Strong background with GIS tools and spatial analysis techniques. Demonstrated expertise in climate variability assessment and the use of climate models. Experience with
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between biodiversity and climate change. The postdoctoral position is embedded in the the collaborative project Past to Future: towards fully paleo-informed future climate projections (P2F; https
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of statistical packages (Stata, R or equivalent). • Experience in spatial analysis and use of Geographic Information Systems (GIS). • Ability to work with large databases and data management tools. Languages
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the services nature provides to people. The position will combine ecological data analysis with statistical and spatial modeling to quantify chemical impacts across multiple levels of biological
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and
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- statistical techniques used in spatial data analysis - scientific programming, e.g. Matlab, R, Python or Julia - designing and/or conducting field measurement campaigns in atmospheric
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geospatial information (land use and cover, biophysical, climate, management practices, etc). • Contribute to the development of spatial and statistical models that describe the interactions between soil and
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evolutionary mechanisms: for example, heterozygote advantage (HA), negative-frequency dependent selection (NFDS) or spatially/temporally fluctuating selection (FS). Recently, new research showing that balancing