Job Announcement: post-doctoral associate in eco-hydrologic modeling, remotely sensed data, and machine learning techniques.

The ARS Cropping Systems and Water Quality (CSWQ) Research Unit is seeking a post-doctoral associate to combine eco-hydrologic modeling, remotely sensed data, and machine learning techniques to 1) determine how select model parameters vary with system variables and climate, and 2) incorporate these relationships into model parameterization and assess the impacts of these adjustments on model results. This research is expected to result in a Decision Support System (DSS) that will facilitate application and use of the Agricultural Policy Environmental eXtender (APEX) model for any field in a pre-defined region. APEX was developed by the USDA-Agricultural Research Service for farm-scale use. This effort to parameterize APEX for multiple farms and fields within a region will increase confidence in model results.

 

The ARS-funded GS-11 position will be stationed at the Columbia, MO, CSWQ Research unit for a 2-year term. This position is associated with the Long-Term Agroecosystem Research (LTAR) network, which includes field-scale experiments evaluating system-level benefits of management practices. The researcher will work with Dr. Claire Baffaut and other scientists at ARS, LTAR, and university collaborators. This position requires US citizenship [or permanent residency (Green Card) with intent to become a citizen] and a Ph.D. degree in biological engineering, hydrology, soil and crop sciences, applied mathematics, or a related field. Knowledge of current methods used in eco-hydrologic modeling, machine learning, and use of remote sensed datasets is required. The ability to read Fortran (for APEX) and write and troubleshoot codes using scientific programming languages (R, Python, C, ...) is a must. The researcher will present results to professional and outreach meetings, and author or co-author peer reviewed manuscripts.

 

To request more information and apply, please contact Claire Baffaut (Claire.Baffaut@usda.gov). Applications should include a full CV, a one-page cover letter outlining your research interests related to the project described above and prior experience in related fields, and the contact information for three references. Minorities, women, veterans, and individuals with disabilities are encouraged to apply.