Characterization of ionospheric dynamics of machine learning techniques

Characterization of ionospheric dynamics through application of machine learning techniques to GPS measurements

Organization
:
National Aeronautics and Space Administration (NASA)

Reference Code
: 0202-NPP-MAR
26-JPL-Helio Sci

How to Apply
:
All applications must be submitted in Zintellect. Please visit the NASA Postdoctoral Program website for application instructions and requirements. A complete application includes a research proposal, three letters of recommendation, and official doctoral transcript documents.

Final date to receive applications
: 3/1/2026 6:00:59 PM Eastern Time Zone

Description

The Earth's upper atmosphere and ionosphere respond dynamically to geomagnetic storms and driving from the Sun. Ionosphere demonstrates local response to a geomagnetic storm superimposed on daily, seasonal and solar cycle dependent variabilities. These different phenomena make characterization of regional ionospheric dynamics a complex problem. New observational capabilities and sophisticated data analysis tools provide an opportunity to improve our understanding of ionosphere dynamics during storms as compared to quiet times.

Specifically, the Total Electron Content (TEC) of the ionosphere is readily available from GPS measurements. Analysis of TEC dynamics over ground-based GPS sites using network analysis is of major interest. Characterizing network topology, connections between TEC measurements at different locations, directionality of the network can lead to better understanding of ionospheric dynamics and advances in space weather forecasting. We seek highly motivated candidates to participate in research on ionospheric dynamics and data analysis.

We are particularly interested in candidates who will analyze satellite and ground-based observations to improve our understanding of space weather. Knowledge of basic signal processing techniques and system science approaches are encouraged.

• Verkhoglyadova, O. P., Komjathy,
  
  A., Mannucci,
  
  A. J., Mlynczak, M., Hunt, L. & Paxton, L.J. (2017). Revisiting Ionosphere-Thermosphere Responses to Solar Wind Driving in Superstorms of November 2003 and 2004. J. Geophys. Res., 122. (Use the "Apply for this Job" box below)..
• McGranaghan, R. M.,
  
  A. J. Mannucci, O. Verkhoglyadova, and N. Malik (2017), Finding multiscale connectivity in our geospace observational system:
  Network analysis of total electron content, J. Geophys. Res., 122, doi:
  10.1002/2017JA024202.
• J. Dods, S.
  
  C. Chapman, and J. W. Gjerloev, Network Analysis of Geomagnetic Substorms Using the Super
  
  MAG Database of Ground Based Magnetometer Stations, JGR, 120, doi:
  10.1002/2015JA021456 (2015)

Jet Propulsion Laboratory, Pasadena, California

Heliophysics Science

Olga Verkhoglyadova
Olga.

Verkhoglyadovaa.gov

Please email npp

• Citizenship: LPR or U.S. Citizen
• Degree:
  Doctoral Degree.