Postdoctoral Fellow: Electromagnetic Modeling & Data

Sandia National Laboratories; California (MO); Hybrid; USD 101,000 - 123,000; Full time

Benefits offered by this job

Relocation assistance

Flexible work arrangements

Generous vacation

Job summary

Sandia National Laboratories seeks a Postdoctoral Appointee to join the Advanced Nuclear Fuel Cycle Technologies Department in Livermore, CA. The role focuses on bridging physics- and data-driven models with large multi-sensor EM datasets, advancing forward/inverse modeling and real-time calibration within a digital twin framework.

The position requires a PhD and DOE Q-clearance eligibility, with 60% onsite work and a hybrid work arrangement.

Qualifications

• PhD in Engineering, Chemistry, Physics, Applied Mathematics or related field.
• Ability to obtain and maintain a DOE Q-level security clearance.
• Experience in computational and electromagnetic modeling/simulation.
• Expertise in machine learning techniques and uncertainty quantification.
• Proficiency with HPC environments and programming in MATLAB, Fortran, Julia, and/or C/C++.
• Proficiency in Python data science stack (NumPy, SciPy, Pandas, Jupyter, Scikit-Learn, PyTorch/TensorFlow).

Responsibilities

• Perform self-directed analysis on provided data sets.
• Present results at team meetings with senior staff.
• Represent Sandia at multi-lab collaboration meetings.
• Publish results at conferences and workshops.
• Collaborate to calibrate predictive models and integrate calibration into digital twins.

Skills

Machine learning

Uncertainty quantification

Electromagnetic modeling

HPC

Python ecosystem

Education

PhD in Engineering or Physics or Applied Mathematics

Tools

MATLAB

Fortran

Julia

C/C++

Python

NumPy

SciPy

Pandas

Jupyter

Job description

Sandia National Laboratories seeks a Postdoctoral Appointee to join the Advanced Nuclear Fuel Cycle Technologies Department in Livermore, CA. The role focuses on bridging physics- and data-driven models with large multi-sensor EM datasets, advancing forward/inverse modeling and real-time calibration within a digital twin framework.

The position requires a PhD and DOE Q-clearance eligibility, with 60% onsite work and a hybrid work arrangement.