Uncertainty Quantification Aircraft Certification Analysis

Uncertainty Quantification for Aircraft Certification by Analysis

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Organization: National Aeronautics and Space Administration (NASA)

Reference Code: 0133-NPP-MAR
26-ARC-Aero

All applications must be submitted in Zintellect. Please visit the NASA Postdoctoral Program website for application instructions and requirements:

How to Apply

| NASA Postdoctoral Program.

A complete application to the NASA Postdoctoral Program includes:

• Research proposal
• Three letters of recommendation
• Official doctoral transcript documents

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

We have an opening for a Postdoctoral Researcher in Uncertainty Quantification (UQ) for simulation-based analysis and design of aerospace vehicles. Uncertainty quantification is a critical technology for enabling the use of high‑fidelity simulations in aircraft certification to reduce reliance on flight testing. The selected candidate will research, develop and apply state-of-the-art nonintrusive statistical methods, including but not limited to dense and sparse quadratures, random sampling and Bayesian statistics to data obtained from high‑fidelity, computational fluid dynamics and multidisciplinary simulations.

The goal is to affordably characterize the uncertainty in output quantities of interest, for example, aerodynamic performance coefficients, range, and acoustic signatures, due to various input uncertainties, such as operating and meteorological conditions, and aircraft shape. Primary areas of interest are estimating and controlling numerical and model‑form errors to improve credibility of numerical predictions, handling discontinuities, such as shocks, that may cause multimodal probability densities, and reducing computational cost of many‑query UQ methods so they can be applied routinely in conjunction with high‑fidelity simulations.

Promising cost‑reduction strategies include but are not limited to multi‑level and multi‑fidelity approximations, and the use of output gradients.

• Research, develop and apply advanced UQ methods for aerospace simulation data.
• Characterize uncertainties in aerodynamic performance, range, acoustic signatures, and other output quantities.
• Estimate and control numerical and model‑form errors to improve prediction credibility.
• Handle discontinuities such as shocks that can lead to multimodal probability densities.
• Reduce computational cost of many‑query UQ methods through multi‑level, multi‑fidelity, and gradient‑based techniques.
• Collaborate with the Computational Aerosciences Branch and stakeholders within the NASA Advanced Supercomputing Division.

• PhD degree in engineering, physics, statistics, or related field.
• Experience programming in Unix/Linux environment using Python, Java, and C/C++.
• Experience with high‑performance computing, parallel programming, and/or GPU programming.

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

Mikeala

Internship

Full‑time

Research, Analyst, and Information Technology

Government Administration

Field of Science:
Aeronautics

Marian Nemec
marian.nemec

Michael Aftosmis
Michael.

Aftosmis