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Opportunity at Naval Research Laboratory (NRL)

Deep Learning Postdoctoral Researcher


Naval Research Laboratory, DC, Information Technology

RO# 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). 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 physics-based machine learning, deep reinforcement learning for decision making, and basic research on training neural networks (see selected references). 

Examples of current projects include physics-embedded machine learning in acoustics, meteorology, and material science, plus the generation of synthetic imagery in computer vision, and deep reinforcement learning for complex decision making.  Anticipated future projects creating methods to improve the generalization ability of deep networks in scientific scenarios   We enjoy close ties with 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 problems.

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 and C.  They should have a broad knowledge of deep learning and experience with DL frameworks, such as PyTorch and TensorFlow.

POC: Leslie N. Smith,

Deep Learning; Machine Learning; Artificial Intelligence; Computer Vision; Decision Aids; Physics-based DL


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


Base Stipend Travel Allotment Supplementation
$89,834.00 $3,000.00
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