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Postdoctoral Research Associate - Earth System Science

Date: Mar 23, 2023

Location: Oak Ridge, TN, US, 37830

Company: Oak Ridge National Laboratory

Requisition Id 10191 


Oak Ridge National Laboratory is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver transformative solutions to compelling problems in energy and security.


We are seeking a Postdoctoral Research Associate who will support the Earth Systems Modeling Group in the Environmental Sciences Division (ESD), Biological and Environmental Systems Science Directorate (BESSD) at Oak Ridge National Laboratory (ORNL). The postdoctoral researcher has expertise in remote sensing of terrestrial ecosystems, machine learning and computational science.


Major Duties/Responsibilities: 

The researcher will help develop new machine learning and Artificial Intelligence algorithms and scalable geospatial analytics methods to study vegetation in natural and managed ecosystems and their response to climate changes and disturbances. They will develop methods for leveraging time series of high-resolution remote sensing datasets from optical/multispectral/radar platforms, multi-sensor data fusion and apply to forested, agricultural, and urban ecosystems at regional to continental scales. They will develop supervised and unsupervised Physics-informed machine learning methods to develop insights into the non-linear processes and drivers of change in ecosystems due to biotic and abiotic stressors and assess their vulnerability and resilience. They will leverage accelerator-based supercomputing platforms (e.g., Summit, Frontier, Aurora, and Perlmutter) to develop scalable computational frameworks.


Basic Qualifications:

  • A PhD in in computational science, Earth system science, environmental science and engineering, ecosystem ecology, hydrology, geography, applied mathematics, or a related field completed within the last 5 years
  • The work will require an applicant with a wide range of skills:

(1) remote sensing of terrestrial ecosystems and ecosystems ecology.

 (2) programming experience in Python, C.

(3) experience with deep learning frameworks such TensorFlow, Keras, PyTorch.

(4) oral and written presentation of results; and (5) ability to work in an integrated team environment


Preferred Qualifications:

  • Experience with FORTRAN, C/C++, and Python languages and with Linux, Git, and LaTeX.
  • Experience with open-source geospatial analysis tools such as GDAL, OGR, QGIS, GRASS, and Python packages for geospatial analysis.
  • Familiarity and parallel programming experience with MPI, OpenMP, OpenACC, and CUDA.
  • Knowledge of commonly used data file formats and conventions (e.g., CF, netCDF, HDF. GeoTiff).
  • Experience with high performance computing, advanced statistical and machine learning methods, and visual data analytics.
  • Excellent written and oral communication skills
  • Motivated self-starter with the ability to work independently and to participate creatively in collaborative teams across the laboratory 
  • Ability to function well in a fast-paced research environment, set priorities to accomplish multiple tasks within deadlines, and adapt to ever changing needs


Please submit three letters of reference when applying to this position. You can upload these directly to your application or have them sent to with the position title and number referenced in the subject line.


Instructions to upload documents to your candidate profile:

  • Login to your account via
  • View Profile
  • Under the My Documents section, select Add a Document


Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be for up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and the availability of funding.


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This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.

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ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.

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