Process-based modeling of seabed geoacoustics and stability
This project seeks researchers to analyze marine seismic data and develop models to better understand seabed geoacoustics, stability, and physical properties globally.
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 (Naval Research Laboratory opportunity)
Funding Amounts: $86,962 stipend plus $3,000 travel allowance; relocation and health insurance benefits available.
Summary: Fellowship supporting postdoctoral research to analyze marine seismic data and develop process-based models for global seabed geoacoustics and stability.
Key Information: Open to U.S. citizens and permanent residents; requires Ph.D. earned within last 5 years; expertise in marine geophysical data and geospatial software preferred.
Description
This fellowship opportunity at the Naval Research Laboratory (NRL) supports postdoctoral researchers in advancing the understanding of seabed geoacoustics and stability through process-based modeling. The research focuses on utilizing marine seismic data—including multi-channel seismic reflection (MCS), ocean-bottom seismometer (OBS), Chirp, and multibeam data—to study seabed geologic and geophysical processes such as tectonics, seismicity, slope stability, and sediment transport.
The project aims to overcome limitations of local-scale seismic data by developing new methods to analyze large legacy datasets and to predict seabed physical properties (e.g., sediment thickness, sound speed, density, velocity structure) on regional to global scales. This is particularly important in areas with sparse observational data like the Arctic, where new seismic data acquisition is costly and challenging.
Applicants are encouraged to creatively improve traditional seismic data processing and interpretation workflows, contribute to global seabed physical property databases, and develop data-driven or process-based predictive models of seabed slope stability. Use of machine learning or other advanced modeling techniques is welcomed but not mandatory.
Applicants should have experience working with various marine geophysical data types and a foundational understanding of global geologic and geophysical processes. Familiarity with geospatial mapping software (e.g., QGIS, GMT) and programming languages (e.g., Python, MATLAB) is expected. Prior experience in machine learning or modeling is advantageous.
