Post Doc - Uncertainty characterization in the human-Earth system
Climate: Past, Present & Future (CL)
Energy, Resources and the Environment (ERE)
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The Joint Global Change Research Institute at Pacific Northwest National Laboratory is soliciting applications for a postdoctoral position advancing uncertainty characterization in the human-Earth system as part of the Global Change Intersectoral Modeling System (GCIMS) Science Focus Area. GCIMS focuses on improving the understanding of the complex interactions among energy, water, land, climate, socioeconomics, and other important human and natural systems at regional to global and seasonal to centennial scales. GCIMS has an emphasis on developing and applying an internally consistent, open-source, and computationally efficient modeling framework that captures the evolution of the integrated human–Earth system. The GCIMS project develops and uses the Global Change Analysis Model (GCAM) along with a suite of dedicated, open-source systems models that include Demeter (global land-use downscaling), Hector (climate emulator), Xanthos (global hydrology), fldgen (climate variability emulator), and Tethys (global water demand downscaling).
The successful applicant will lead the analysis using the Global Change Analysis Model (GCAM; http://jgcri.github.io/gcam-doc/index.html) and related models (e.g., Hector, Xanthos). Specifically, this scientist will focus on generating and analyzing large ensembles of GCAM simulations that quantify the effect of changes in human and Earth system drivers on the energy, water, land system. The scientist is expected to use scenario discovery tools and possibly other machine learning techniques in this analysis. The scientist is expected to lead multiple peer-reviewed publications on this work; there will be many opportunities to work with the multi-disciplinary GCIMS team as well as collaborators in other national labs and universities.
Candidates must have received a PhD within the past five years (60 months) or within the next 8 months from an accredited college or university.
Preferred qualifications include the receipt of a PhD after January 1 2017; a background in engineering, physical sciences, applied mathematics, computational science, economics, hydrology, or related field; strong verbal and written communication skills; a willingness to work both independently and within a collaborative team environment, and a proven capability to publish in peer-reviewed journals. Previous graduate-level statistical coursework, as well as substantial programming experience, are required; experience with R or python is preferred but clear and well commented code in other languages is welcome.
Required information for application: A cover letter, CV, and individually-authored coding sample in R or Python. Applications that do not have all this requested information will not be considered. Please upload as one pdf file.
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