Inverse ML Time-Evolving Glacier Flow from Space

Organization

National Aeronautics and Space Administration (NASA)

0240-NPP-NOV
26-JPL-Earth Sci

Earth Science

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. Although confidence that ice loss will accelerate into the future is high, the magnitude of this acceleration remains uncertain because of limited understanding of the rate‑controlling processes that modulate ice flow.

A recent explosion in the number of sensors capable of measuring ice flow from space, and the maturation of projects targeting comprehensive records of ice flow (e.g., NASA’s  project), create a unique opportunity to apply machine learning and neural network methodologies, in conjunction with simplified ice‑sheet models, to advance our understanding of ice‑sheet basal processes and their evolution through time.

To that end, we seek an NPP applicant to develop methodologies that 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 may 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, depending on the candidate’s desired research focus.

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://(Use the "Apply for this Job" box below)..

• U.S. Citizens
• U.S. Lawful Permanent Residents (LPR)
• Foreign Nationals eligible for an Exchange Visitor J‑1 visa status
• 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

• 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 programming skills with a preference for Python and Julia languages
• Strong record of peer‑reviewed publications
• Demonstration of open science practices

Alex Gardner (Sea Level and Ice Group), Email:
Alex.

S.Gardnera.gov, Phone:

Contact: npp