Post Doctorate Research Associate - Urban-Resolving Earth System Modeling

Overview

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. Our Science & Technology directorates include National Security, Integrated Discovery Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.

The Integrated Discovery Sciences Directorate (IDSD) leads fundamental research across biology, chemistry, earth and environmental sciences, materials science, advanced computing, artificial intelligence, quantum information science, mathematics, autonomy, and DOE national user facilities. Our vision is to accelerate scientific discovery by observing, understanding, simulating, predicting, and controlling dynamic biotic-abiotic processes and interactions within complex biological, chemical, material, and Earth systems. We intentionally connect disciplines to advance fundamental discovery science, develop transformative scientific capabilities, and address the nation's most important challenges in energy, environmental resilience, biotechnology, advanced manufacturing, health, and national security.

The Atmospheric, Climate, and Earth Sciences (ACES) Division comprises multidisciplinary teams working to advance the predictive knowledge of Earth systems. The ACES team includes researchers at every career stage who collaborate across disciplines to address some of the most pressing challenges which are critical to ensure a robust U.S. energy system. Scientists in ACES provide their expertise to major sections of the Department of Energy's Office of Science, Biological and Environmental Research program, including the Atmospheric Radiation Measurement (ARM) user facility, the Atmospheric System Research program, and the Earth & Environmental Systems Modeling program.

The Earth System Modeling Group at PNNL seeks a postdoctoral research associate to improve representation of urban evolution, or change in urban extent and properties over time, in Earth system models (ESMs). The successful candidate will perform global-scale analyses of satellite observations and satellite-derived datasets to generate ensembles of temporally varying biophysical parameters for urban areas, both historical and for future scenarios.

These datasets will then be incorporated into DOE’s Energy Exascale Earth System Model (E3SM) to run sensitivity experiments on the role of urban parameter inputs on local to regional meteorological states across scales. Expertise in statistical analysis (particularly machine learning methods) relevant to time-series forecasting, remote sensing, and previous experience running E3SM or other ESMs is desired.

Minimum Qualifications:

• 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.

• Ph.D. degree in Earth Sciences, Atmospheric Sciences, Meteorology, Physics, Environmental Science, Environment Engineering, Statistics, or a related field.
• Familiarity with high-performance computing, Earth system modeling, and statistical analysis.
• Experience with computer languages usually used for modeling and analysis (Fortran, Python, NCL, C++, etc.).
• Demonstrated expertise in urban-scale modeling and analysis.
• Demonstrated expertise in satellite remote sensing, especially using Google Earth Engine.
• Experience with computer languages used for geospatial modeling and analysis (GDAL, Python, JavaScript for Earth Engine, R, etc.).
• Demonstrated experience in using Earth system models like CESM and/or E3SM.
• Demonstrated experience in modifying the source code of models to develop new capability.
• Demonstrated experience in advanced statistical analysis and/or machine learning techniques.
• Knowledge of novel data-model integration techniques.
• Ability to think critically and pursue original ideas independently.

Not applicable.

This is not a Testing Designated Position (TDP).

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