All applications must be submitted in Zintellect

Description:

One of the most consequential impacts of climate changes is an increase in ice loss from glaciers and ice sheets. Such losses are largely responsible for increasing rates of sea level rise. Despite confidence that ice loss will accelerate into the future, the magnitude of such acceleration is still highly uncertain due to still limited understanding of the rate controlling processes that modulate ice flow. A recent explosion in the number of sensor capable of measuring ice flow from space, and the maturation of projects targeted at extracting comprehensive records of ice flow (e.g. NASA’s ITS_LIVE project), create a unique opportunity to apply machine learning and neural network methodologies, in conjunction with simplified ice sheet models, to advance understanding of ice sheet basal processes and their evolution through time. To that end we are seeking an NPP applicant to develop methodologies to invert for time-evolving basal friction from massive volumes of surface flow observations. The successful candidate will work under the supervision of Dr. Alex Gardner (Sea Level and Ice Group) and will have the opportunity to collaborate with researchers from across the lab and with close collaborators at M.I.T., Brown University, University of Grenoble, and the California Institute of Technology, dependent on the candidate’s desired research.

Field of Science: Earth Science

Advisors:
Alex Gardner
Alex.S.Gardner@jpl.nasa.gov
(818) 354-3477

Applications with citizens from Designated Countries will not be accepted at this time, unless they are Legal Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export-control.

Eligibility is currently open to:

• U.S. Citizens;
• U.S. Lawful Permanent Residents (LPR);
• Foreign Nationals eligible for an Exchange Visitor J-1 visa status; and,
• Applicants for LPR, asylees, or refugees in the U.S. at the time of application with 1) a valid EAD card and 2) I-485 or I-589 forms in pending status

Preferred Qualifications for the Ideal Applicant

• PhD in Cryosphere Sciences or similar field
• Strong background in machine learning and neural networks
• Advanced understanding of remote sensing principles
• Experience working with large remote sensing datasets
• Strong programing skills with a preference for Python and Julia languages
• Strong record of peer-reviewed publications
• Demonstration of open science practices

• Degree: Doctoral Degree.