Terrestrial Ecosystem Science and Technology (TEST) group

Postdoctoral Research Associate: Remote Sensing
and Data Assimilation


The Terrestrial Ecosystem Science and Technology (TEST) group is seeking a post-doc interested in reducing uncertainty in the modeling of the terrestrial carbon cycle through remote sensing and data assimilation approaches. This position is part of a larger project to develop a terrestrial carbon cycle data assimilation framework, focused initially on North America, using the PEcAn model informatics system (PEcAn, http://pecanproject.org/). This system will employ formal Bayesian model-data fusion between bottom-up process-based ecosystem models and multiple data sources, including remote sensing data, to estimate key carbon pools and fluxes.

BNL policy requires that Research Associate appointments may be made to those who have received their doctoral degrees within the past five years. Two years of funding is available.

Essential duties and required skills

The candidate will work with in collaboration with researchers at Boston University (BU) to iteratively extend the PEcAn data assimilation system to ingest a wide range of remotely sensed and ground data with the goal of fusing and reconciling multiple data streams into a continental-extent carbon cycle (pools and fluxes) data product.

  • Work with multiple land surface models to explore the impact of the inclusion of different products on carbon cycle uncertainties with the aim of improving carbon monitoring, reporting, and verification
  • Responsible for the inclusion of NASA and other remote sensing data products into the data assimilation framework
  • Work with collaborators to extend and enhance the assimilation approach
  • Responsible for leading and participating in the development of project reports and peer-reviewed manuscripts

Required Knowledge, Skills and Abilities

Prospective candidates should be willing to work in a collaborative team environment, have good written and oral communication skills, and a record of publication in high quality internationally recognized journals.

  • PhD in Environmental Science (ecology, geography, remote sensing, environmental monitoring, atmospheric science, earth science, or related field)
  • Experience with the R programming environment and at least one of the following topics is required (along with interest in learning the others):
    • Remote sensing
    • Ecosystem or land surface modeling
    • Bayesian statistics, or
    • Ensemble filtering approaches (e.g. EnKF)

Preferred Knowledge, Skills and Abilities

  • Effective written and oral communication skills
  • Demonstrated ability and willingness to work collaboratively in team environment
  • Experience with high-performance or distributed computing environments

To apply please use this link or go to https://jobs.bnl.gov/ and put in the position number (1142) into the keyword search.

posted: 08 February 2017     Please mention EARTHWORKS when responding to this advertisement.