[ES_JOBS_NET] Postdoctoral Research Associate: Remote Sensing and Data Assimilation

[Serbin, Shawn]

Job positing:  https://www.bnl.gov/envsci/testgroup/jobs.php

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.

Essential duties and required skills

The candidate will work at BNL 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


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)


To apply please enter the position number 1142 into the Keyword field at the
BNL careers page (https://jobs.bnl.gov/) or follow this link directly:
https://jobs.bnl.gov/job/upton/post-doc-remote-sensing-and-data-assimilation-environmental-sciences/3437/6060889


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