[ES_JOBS_NET] Postdoctoral Researcher Associate/Assistant Research Scientist in Land-Atmosphere Interactions and Model Benchmarking

[Serbin, Shawn P. (GSFC-6180)]

Summary
The Biospheric Sciences Laboratory (Code 618) at NASA’s Goddard Space Flight
Center is seeking a highly motivated and talented postdoctoral researcher to join our
dynamic team. Our group focuses on the study of land-surface dynamics, carbon
cycling and the fluxes of greenhouse gases between terrestrial ecosystems and the
atmosphere, as well as the role of biosphere-atmosphere interactions on global climate.
We utilize a range of tools, including multi-scale remote sensing Earth observations and
modeling to advance our understanding of land surface processes and couplings with
the atmosphere.

Position Description
This position involves carrying out cutting-edge research into the characterization of
land surface processes and the modeling of terrestrial carbon cycling and surface-
atmosphere fluxes of greenhouse gasses, water, and energy. This will involve
collaboration with a diverse team of scientists on topics including the scaling of carbon
cycle processes; evaluation of satellite greenhouse gas retrievals using surface
observation networks and multi-scale remote sensing data; and analysis of regional
drivers of greenhouse gas fluxes and plant and soil carbon pools using observations
and process modeling. The work is expected to contribute to relevant ongoing projects,
NASA science teams and ongoing activities related to future satellite mission planning,
process model development needs, and the publication of peer reviewed articles in high
quality journals, as well as contributions to proposal and project development. All work
will be performed in close collaboration with scientists at GSFC’s Biospheric Sciences
Laboratory and the Global Modeling and Assimilation Office (GMAO).

Qualifications

  *   Ph.D. in the Earth, atmospheric, or environmental sciences, ecology, forestry, computer science, or related fields.
  *   Expertise in programming in Fortran, Python, R, Julia, or C/C++ or similar languages.
  *   Background in statistical modeling and/or machine learning is desirable.
  *   Experience working in high-performance computing environments.
  *   Excellent written and verbal communication skills.
  *   Ability to work independently and collaboratively.
  *   Proven track record of presenting research at conferences.

Application
To apply, send your c.v., cover letter and the names of three references to  anegri at umd.edu

Position open until filled.
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