The Cooperative Institute for Research in Environmental Sciences (CIRES) and the NOAA Global Monitoring Laboratory (GML) in Boulder, Colorado, announce an opportunity for a Post-Doctoral Associate to improve land carbon cycle modeling using atmospheric carbon cycle observations and satellite data constraints. This position involves using atmospheric carbon cycle observations (e.g. carbon dioxide and its carbon-13 isotopologue, and carbonyl sulfide) and satellite remote sensing data (e.g. land surface temperature, solar induced fluorescence) to evaluate and optimize parameter selection in land carbon cycle models. The goal of this research is to optimize land carbon cycle models for drought monitoring and forecasting hydroclimate in the western United States, and is funded through the NOAA Modeling, Analysis, Predictions and Projections program. The successful candidate will work on a NOAA MAPP Drought research grant and interact closely with principal investigators in the Carbon Cycle and Greenhouse Gases group at the NOAA Global Monitoring Laboratory and at the University of Colorado, Boulder.
What Your Key Responsibilities Will Be
- (80%) Develop a Bayesian framework to evaluate an ensemble of land carbon cycle simulations to evaluate parameter choices influencing carbon uptake and release for plant-drought interactions. This will involve:
* Preparing remote sensing and in situ data for use in a Bayesian modeling system, including consideration of measurement and model uncertainty in the construction of cost functions.
* Designing and executing model experiments aimed at understanding constraints imposed by different types of observations at various spatial and temporal scales. - (10%) Lead and contribute to the publication of research results in the peer-reviewed literature.
- (10%) Present research findings at national and international conferences.
What We Require
- A Ph.D. in Atmospheric Science, Earth System Science, Data Science, Applied Mathematics or a related field.
- Demonstrated experience with numerical modeling, preferably on high-performance computing systems.
- Demonstrated experience with analyzing and visualizing large datasets using scientific programming languages such as MATLAB, Python or R.
What You Will Need
- Ability to critically interpret and diagnose model output.
- Skill in manipulation and analysis of large and complex observational and model datasets.
- Familiarity with both compiled and high-level programming languages.
- Ability to work as part of an inclusive and productive team as well as the ability to formulate independent ideas.
What We Would Like You to Have
Please note that while the position details both required and preferred skills and experience, we invite applicants to apply even if they do not have the preferred skills and experience outlined in this section. If you meet the requirements and have passion for the work, you are encouraged to apply. We encourage on the job training for any additional skills or knowledge that become relevant to the position.
- Experience working with biosphere models and data assimilation techniques.
- Experience with programming in Fortran.
- Knowledge of Bayesian and ensemble Kalman filter techniques.
- An interest in investigating ways to improve our knowledge of the land carbon cycle and to exploit multiple observational constraints for drought monitoring and prediction.
- Experience in publishing peer-reviewed journal articles, technical reports, and presenting research findings at scientific conferences.