Physics Informed Data Analytics and Modeling for Dynamical Systems
Develop computational models for complex systems using physics-based methods, data analytics, and machine learning to understand and predict behavior under uncertainty.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: February 1, 2025 | May 1, 2025 | August 1, 2025 | November 1, 2025
Funding Amounts: Stipend approximately $99,200 per year with $3,000 travel allowance; typical tenure 2-3 years.
Summary: Supports postdoctoral research to develop computational models combining physics-based methods and data analytics for complex dynamical systems under uncertainty.
Key Information: Open to U.S. citizens and permanent residents; requires prior contact with Research Adviser; relocation and health insurance benefits included.
Description
This fellowship opportunity, offered through the NRC Research Associateship Programs at the Naval Research Laboratory (NRL) in Washington, DC, focuses on advancing computational approaches to model the state and response of complex dynamical systems under uncertainty. The research involves multi-physics systems described by parameterized partial differential equations (PDEs) and requires expertise in numerical modeling and uncertainty quantification.
Candidates are expected to integrate modern data analytics techniques, including machine learning and deep learning, with physics-based reduced order models such as principal component analysis (PCA) or proper orthogonal decomposition (POD). The role includes designing, implementing, verifying, and validating algorithms that enhance understanding and prediction of system behavior, potentially contributing to digital twin and digital thread technologies, inverse problems, and parameter estimation.
This opportunity is ideal for postdoctoral researchers with a strong background in applied mathematics, computational physics, engineering, or related fields who are interested in interdisciplinary research combining physics-based modeling and data-driven analytics.
