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RAP opportunity at Naval Research Laboratory     NRL

Investigating Large Generative Models and/or Physics-based Neural Network Modeling


Naval Research Laboratory, DC, Information Technology

opportunity location
64.15.18.B8506 Washington, DC 203755321


name email phone
Leslie Neil Smith 410-209-9961


The U.S. Naval Research Laboratory’s renowned Navy Center for Applied Research in Artificial Intelligence is seeking an excellent postdoctoral researcher in machine learning, computer science, applied mathematics, or physics to work closely with senior scientists on pure and applied science projects related to Deep Learning (DL) and generative AI, such as Large Generative Models. The NRL Deep Learning team’s research involves basic, practical, and applied research, development, and evaluation of innovative deep learning methodologies. Our current research foci include the use of Large Language Models (LLMs) for Navy use cases (such as for decision-making/mission planning), physics-based machine learning, and basic research on training neural networks (see selected references). 

For our current projects, we're exploring the use of open-source LLMs on internal servers for complex decision-making, utilizing physics-enhanced neural networks in fields like acoustics, meteorology and material science, and enhancing the adaptability/generalization abilities of neural networks to handle vast data sets common in physics studies.  We enjoy close ties with researchers in academia and the military, who can benefit from the prototypes that we develop. Our group often includes summer interns and visiting summer researchers.

Since its inception in 1981, the Navy Center for Applied Research in Artificial Intelligence (NCARAI) has been involved in both basic and applied research in artificial intelligence, cognitive science, autonomy, and human-centered computing. NCARAI, part of the Information Technology Division within the Naval Research Laboratory, is engaged in research and development efforts designed to address the application of artificial intelligence technology and techniques to critical Navy and national security.

Selected References:

Smith, Leslie N. "Cyclical learning rates for training neural networks." In Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on, pp. 464-472. IEEE, 2017.

Smith, Leslie N., and Nicholay Topin. "Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates." arXiv preprint arXiv:1708.07120 (2017).

Smith, Leslie N. "General Cyclical Training of Neural Networks." arXiv preprint arXiv:2202.08835 (2022).

Smith, Leslie N. "Cyclical Focal Loss." arXiv preprint arXiv:2202.08978 (2022).


A candidate should have excellent programming skills in Python.  They should have a broad knowledge of deep learning and experience with DL frameworks, such as PyTorch and TensorFlow.

POC: Leslie N. Smith,

key words
Generative AI, Large Generative Models, Deep Learning; Machine Learning; Artificial Intelligence; Computer Vision; Decision Aids; Physics-based neural networks


Citizenship:  Open to U.S. citizens and permanent residents
Level:  Open to Postdoctoral applicants


Base Stipend Travel Allotment Supplementation
$94,199.00 $3,000.00
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