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Field
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runoff under climate change, and Analyze changes in marine ecological (phytoplankton) communities during and after freshwater extremes (low-salinity pulses) at multiple spatial and temporal scales
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Qualifications: We seek exceptionally strong candidates with a PhD in statistics, biostatistics, computer science or a related field. Candidates with demonstrated accomplishment in academic research, as can be
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the workpackage team and collaborating with partners across Europe: data sharing, code exchange, joint publications. Your qualities You hold a PhD (or near completion) in statistics, applied mathematics, data
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partners, and monitoring budgets. Required Qualifications : PhD: A PhD in digital public health, urban health, data science applied to health or urban planning, smart urban systems, or a related field
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at larger spatial units. You will work with established field experiments and full-scale facilities already established by farmers in various locations throughout Denmark and the boreal region of Europe. You
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over the course of the project. References: - Deneu B et al (2021) Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput
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datasets (e.g., forest inventory, remote sensing, climate data) across Washington, Oregon, and/or California. Conducting advanced spatial and statistical analyses using methods implemented in R or similar
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with R, statistical and spatial analysis Excellent communication and teamwork skills Excellent organizational skills Experience with field work in post-disturbance forest conditions (desired) Experience
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
/or machine learning/artificial intelligence algorithms. Projects may also include work focused on the analysis of spatial and geographic data and work extrapolating results to different spatial scales
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Preferred Qualifications : Competitive candidates should be highly motivated and possess a PhD in quantitative/spatial ecology of wildlife or a related discipline with a strong quantitative emphasis (ABD