This postdoctoral fellow position is for a recent PhD graduate who is interested in developing reduced order models for earth system models. The successful candidate will work on a project titled “Multiscale reduced order models for integrated earth system models” funded by the Department of Energy. He/She will combine multiple reduced order modeling techniques from different disciplines and apply these techniques to subsurface flow models and, subsequently, to other submodels within the Community Land Model (CLM) and Community Earth System Model (CESM). He/She will also develop methods to accurately quantify the modeling errors and uncertainties of these reduced order models, and study their impacts on uncertainty quantification.
The ideal candidate must have excellent knowledge in one or more of the following areas: statistical techniques, model order reduction techniques, Gaussian process regression and numerical land-surface models. He/She should be familiar with Fortran or C++, and have demonstrated ability to develop complex scientific codes. He/She must be able to work independently with minimal supervision, and proactively propose and test new ideas that are relevant to the project. Good oral and written communication skills are expected.

Responsibilities:
 

• Develop new reduced order models for subsurface and land-surface models.
• Quantify errors and uncertainties in the reduced order models.
• Develop a hierarchy of reduced order models and study the propagation of modeling errors and uncertainties.
• Work with climate scientists to incorporate the models into CLM and CESM.
• Author technical reports and peer-reviewed journal articles.

Qualifications:

• Doctoral degree in applied mathematics, earth sciences, engineering or other relevant disciplines.
• Strong mathematical foundation in model order reduction techniques and statistical modeling techniques such as Gaussian process regression.
• Ability to work with domain scientists to identify modeling requirements and express these requirements mathematically.
• Programming experience in Fortran or C++.
• Familiar with subsurface modeling, CLM, and CESM.
• Has prior experience in the development of large-scale subsurface flow simulators similar to pFLOTRAN, Amanzi or TOUGH.
• Motivated to work independently and contribute proactively to the project.
• Capable of giving clear oral and written presentation of results.

NOTES: This is a 1-year term appointment with the possibility of renewal for up to 5 years based upon satisfactory job performance, continuing availability of funds, and ongoing operational needs. This position requires completion of a background check. Salary for post-doctoral positions depends on years of experience post-degree.

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