Oak Ridge National Laboratory is seeking a Postdoctoral Research Associate to support the Earth Systems Modeling Group in the Environmental Sciences Division (ESD), Biological and Environmental Systems Science Directorate (BESSD). The successful candidate will have expertise in remote sensing of terrestrial ecosystems, machine learning, and computational science. They will develop new machine learning and Artificial Intelligence algorithms, scalable geospatial analytics methods, and apply them to study vegetation in natural and managed ecosystems and their response to climate changes and disturbances.
- Company: Oak Ridge National Laboratory
- Location: Oak Ridge, TN, US, 37830
- Research Area: Remote Sensing of Terrestrial Ecosystems, Machine Learning, and Computational Science
- Designation: Postdoctoral Research Associate
- Requisition ID: 10191
- A PhD 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
- Experience in remote sensing of terrestrial ecosystems and ecosystems ecology
- Programming experience in Python, C
- Experience with deep learning frameworks such as TensorFlow, Keras, PyTorch
- Excellent written and oral communication skills
- Ability to work in an integrated team environment
|Education||PhD 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|
|Skills||Remote sensing of terrestrial ecosystems and ecosystems ecology, programming experience in Python, C, experience with deep learning frameworks such TensorFlow, Keras, PyTorch|
|Other||Oral and written presentation of results; ability to work in an integrated team environment|
- 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
- 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
The successful candidate will help develop new machine learning and Artificial Intelligence algorithms, scalable geospatial analytics methods, and apply them 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 them 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.
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Last Date for Apply: 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.
Note: Applicants cannot have received their Ph.D. more than five year