Postdoctoral Appointee: Credible Scientific Machine Learning & Data Driven Analysis, CA Onsite

Postdoctoral Appointee:
Credible Scientific Machine Learning & Data Driven Analysis, CA Onsite

Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting‑edge work in a broad array of areas. Some of the main reasons we love our jobs:

Challenging work with amazing impact that contributes to security, peace, and freedom worldwide

Extraordinary co‑workers

Some of the best tools, equipment, and research facilities in the world

Career advancement and enrichment opportunities

Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten‑hour days each week) compressed workweeks, part‑time work, and telecommuting (a mix of onsite work and working from home)

Generous vacation, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance*

World‑changing technologies. Life‑changing careers. Learn more about Sandia at: http://www.sandia.gov

* These benefits vary by job classification.

What Your Job Will Be Like:

We are seeking a Postdoctoral Appointee. Join a multidisciplinary team to advance trustworthy scientific machine learning (SciML) and data‑driven analysis for computational science applications.

On any given day, you may be called on to:

Develop and evaluate ML‑based surrogates, reduced‑order models, and embedded data‑driven components within physics‑based simulations

Design and execute verification, validation, uncertainty quantification, robustness testing, explainability, and interpretability studies

Apply feature‑extraction and pattern‑recognition methods: wavelet analysis, tensor/matrix decompositions, energy‑based features, anomaly detection, and supervised/unsupervised learning to images, microstructures, sensor data, and high‑dimensional simulation outputs

Collaborate with Sandia PIs, national laboratories, and academic partners to integrate, compare, and benchmark data‑driven methods against high‑fidelity models and experiments

Due to the nature of the work, the selected applicant must be able to work onsite in Livermore, CA.

Qualifications We Require:

PhD (conferred within 5 years) in applied mathematics, computational science, engineering, statistics, computer science, or a related field

Expertise in machine learning, scientific machine learning, uncertainty quantification, or statistical learning

Experience developing or evaluating ML methods for simulation, image, signal, or high‑dimensional data

Ability to obtain and maintain a DOE Q‑level security clearance

Qualifications We Desire:

Hands‑on experience with SciML surrogates, VVUQ, independent model evaluation, robustness, and explainable/interpretable ML

Proficiency in wavelet and spectral analysis, tensor/matrix decomposition, and energy‑based feature extraction

Familiarity with supervised/unsupervised learning, classification, clustering, and anomaly detection

Skilled in Python and ML libraries (PyTorch, JAX, Tensor Flow, scikit‑learn, Num Py, Sci Py, pandas, PyWavelets, OpenCV, etc.)

About Our Team:

The Quantitative Modeling and Analysis Department conducts research, development, and systems engineering in computer science and engineering to address important, complex national security problems. Key research areas include uncertainty quantification, optimization, inference modeling, data analysis, and algorithm development. Much of our application development work focuses on verification, validation and uncertainty quantification of complex problems. We provide trusted design and software development for large, operations software as well as detailed…