Multi-Modular AI Agent ISSM Modeling, Data Fusion, and Geophysical Analysis

About the NASA Postdoctoral Program

The NASA Postdoctoral Program (NPP) offers unique research opportunities to highly talented scientists to engage in ongoing NASA research projects at a NASA Center, NASA Headquarters, or at a NASA-affiliated research institute. These one- to three-year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology.

The Ice Sheet and Sea Level System Model (ISSM) is an integral component of the Integrated Modeling Virtual Institute (IMVI), a recent NASA initiative aimed at synergizing models of the Earth system and its components. ISSM is a modular C++ codebase with MATLAB and Python interfaces, designed to simulate Cryosphere, Solid Earth, and Sea Level processes, either in coupled or standalone configurations.

The software can assimilate large datasets and perform adjoint computations, inverse modeling, and uncertainty quantification. It has enabled breakthrough scientific discoveries and supported mission development as well as societal applications.

The overarching goal of this project is to enhance ISSM’s capability, scalability, and accessibility by developing a multi-modular, agentic AI framework tailored to scientific modeling workflows. Rather than deploying AI as a black-box predictor, we propose a physics-aware AI agent architecture that interfaces directly with ISSM’s modular components. The agent will operate at multiple levels.

• Infrastructure assistance: automating cross‑platform installation, dependency management, compilation, and HPC/cloud optimization.
• Modeling intelligence: supporting adaptive mesh refinement, parameter selection/sampling, solver tuning, and convergence diagnostics.
• Computational acceleration: building emulator modules using neural operators and reduced‑order models to approximate expensive forward and adjoint simulations while preserving underlying physics.
• Uncertainty‑aware inference: combining physics‑informed learning for regularization with probabilistic generative models to approximate posterior parameter distributions and guide efficient exploration of inversion problems.

Earth Science

• Surendra Adhikari – adhikaria.gov –
• Eric Larour – Eric.
  
  Laroura.gov –

Applications with citizens from Designated Countries will not be accepted 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

• Degree:
  Doctoral Degree.

Questions about this opportunity? Please email npp

Contact person:
Mikeala