JPL Post Doc Improving geophysical surface retrievals at the global scale fromOptical and SAR satel
Company: NASA JPL
Posted on: November 19, 2022
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The plethora of orbiting optical and SAR sensors has provided an
unprecedented view of our Earth and its processes. With the recent
launch of Landsat 9 and the upcoming launches of NISAR,
Sentinel-1C, and Sentinel-2C, this view will become significantly
more precise not only because the volume of this imagery will
greatly increase but also because the quality of the imagery is
built of the success of previous space-borne missions. It is of
great scientific interest to develop robust and reliable algorithms
that utilize this forthcoming imagery in synchronicity to improve
our view of the Earth. SAR and optical sensors can complement each
other to provide a picture not possible from an optical or SAR
The postdoc will:
* Develop algorithms to improve geophysical retrievals at the
global scale, harmonizing optical and SAR sensors.
The group's primary interest is to develop globally-applicable
methods to detect surface disturbances and map inundation extents,
both of which are important for Earth Science System Modeling and
The models and pipelines may be adapted into the NASA JPL
Observational Products for End-Users from Remote Sensing Analysis
(OPERA) project, and therefore, the postdoc's work may have a
lasting impact on the broader Earth Science community.
Specifically, OPERA products will be leveraged by decision-makers
from federal agencies and the science community for a broad range
of application topics including those related to a disaster,
resource management, and monitoring.
Applicants should have:
* Ph. D. in machine learning, statistics, computer science,
physics, computational mathematics, or a related field.
* Strong background in the theory and practice of machine learning
or applied statistics to remote sensing retrievals, particularly as
it relates to image understanding, data fusion, and uncertainty
* Given a research problem, an applicant is expected to scope out
the features of interest from the plethora of available
remote-sensing data, develop large-scale training and validation
pipelines, develop architectures for data fusion, fine-tune
state-of-the-art neural networks and ML models.
* Be able to report their findings in a mathematically and
statistically sound fashion.
* Have a strong grasp of the python ecosystem especially its
statistical (e.g. scipy/numpy) and machine learning libraries (e.g.
scikit-learn, pytorch, tensorflow), jupyter, git, and preferably
* Be comfortable quickly diving into different data formats (e.g.
Tiff, NetCDF, HDF5) as they will be utilizing a variety of remotely
sensed data such as optical data from Planet, Worldview, Landsat,
Sentinel-2, and SAR data from Sentinel-1, ALOS-2, NISAR.
* Experience working with GPU's (CuPy, Numba, or CUDA), curating
data from public APIs, developing large scale GIS products, and
development using AWS are all considered a plus.
* Experience using remote sensing data for retrievals of surface
disturbances, surface deformation, and surface water is considered
The appointee will carry out research in collaboration with the JPL
advisor, David Bekaert. The postdoc will interact with other OPERA
Project Science team members from JPL, USGS, GSFC, and ASF.
Applicants may be subject to additional program requirements by
NASA. Postdoc positions are awarded for a minimum of one-year
period and may be renewed up to a maximum of three years.
Candidates should submit the following to this site: CV,
representative publications, contact information for three
references, and a cover letter stating their research
accomplishments and interests.
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The Jet Propulsion Laboratory is a federal facility. Due to rules
imposed by NASA, JPL will not accept applications from citizens of
designated countries or those born in a designated country unless
they are Legal Permanent Residents of the U.S or have other
protected status under 8 U.S.C. 1324b(a)(3). The Designated
Countries List is available here.
Keywords: NASA JPL, Pasadena , JPL Post Doc Improving geophysical surface retrievals at the global scale fromOptical and SAR satel, Other , Pasadena, California
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