Position Description:

The Upper-Atmosphere Physics group in the Remote Sensing Division at the U.S. Naval Research Laboratory (NRL) is seeking a talented and motivated postdoctoral fellow to conduct research in the application of advanced machine learning/artificial intelligence techniques to the simulation of upper atmospheric (mesosphere/thermosphere) and ionospheric physics. This position will focus on the application and development of machine learning techniques for the emulation of complex physical processes that induce coupling between these atmospheric regions.   

The long-term goal of our group is the development of a fully coupled, ground-to-space, whole atmosphere numerical weather model and data assimilation system that integrates both neutral and plasma dynamics. The successful candidate will play a key role in this effort by leveraging NRL's extensive datasets and first-principles physics models to create novel machine learning-based parameterizations of critical processes. This is a unique opportunity to work at the intersection of atmospheric science, plasma physics, and artificial intelligence with a leading research group that has access to unparalleled computational resources and proprietary datasets.

Responsibilities:

• Design, develop, and train machine learning models to emulate specific physical processes in the upper atmosphere and ionosphere.
• Analyze and interpret data from a variety of observational platforms and physics-based models.
• Collaborate with a multidisciplinary team of scientists and engineers.
• Contribute to the development of next-generation atmospheric and ionospheric models.

Qualifications:

Required:

• A Ph.D. in Physics, Atmospheric Science, Space Physics, Engineering, Computer Science, or a related field.
• Strong programming skills, particularly in Python.
• A solid foundation in upper atmospheric and/or ionospheric physics.
• Excellent written and oral communication skills.

Desired:

• Experience with machine learning frameworks (e.g., PyTorch and/or TensorFlow).
• Familiarity with high-performance computing (HPC) environments.
• Experience working with large geophysical datasets.
• Knowledge of atmospheric and/or ionospheric models (e.g., WACCM-X, TIE-GCM, SAMI3).

Machine learning, Artificial intelligence, Space weather, Upper atmosphere, numerical weather prediction

citizenship

Open to U.S. citizens and permanent residents

level

Open to Postdoctoral applicants

Additional Benefits

relocation

Awardees who reside more than 50 miles from their host laboratory and remain on tenure for at least six months are eligible for paid relocation to within the vicinity of their host laboratory.

health insurance

A group health insurance program is available to awardees and their qualifying dependents in the United States.