Investigating Large Generative Models and/or Physics-based Neural Network Modeling
The Navy seeks a postdoc to research large language models, physics-based machine learning, and neural network adaptability for naval applications.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: May 1, 2025 (Next deadline)
Funding Amounts: $99,200 stipend plus $3,000 travel allowance; typical postdoctoral fellowship duration 2-3 years
Summary: Postdoctoral fellowship at the U.S. Naval Research Laboratory to conduct research on large generative models, physics-based machine learning, and neural network adaptability for naval applications.
Key Information: Open to U.S. citizens and permanent residents with a Ph.D. earned within the last 5 years; requires strong Python and deep learning framework skills (PyTorch, TensorFlow).
Description
The U.S. Naval Research Laboratory’s Navy Center for Applied Research in Artificial Intelligence (NCARAI) invites applications for a postdoctoral research fellowship focused on advancing deep learning and generative AI technologies. The research opportunity centers on:
- Large Language Models (LLMs) tailored for Navy-specific use cases such as decision-making and mission planning.
- Physics-based machine learning approaches integrating domain knowledge from acoustics, meteorology, and material science.
- Enhancing neural networks' adaptability and generalization to large, complex datasets typical in physics and applied sciences.
The NCARAI has a long-standing history (since 1981) of conducting both fundamental and applied AI research to support Navy and national security missions. The postdoctoral researcher will collaborate closely with senior scientists, contributing to both theoretical and practical advancements in deep learning methodologies.
Applicants should have strong programming skills in Python and experience with deep learning frameworks like PyTorch and TensorFlow. The research environment encourages collaboration with academia and military researchers and includes opportunities for mentoring summer interns and visiting researchers.
Selected References by Adviser Leslie N. Smith:
- Cyclical learning rates for training neural networks (WACV 2017)
- Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates (arXiv 2017)
- General Cyclical Training of Neural Networks (arXiv 2022)
- Cyclical Focal Loss (arXiv 2022)
