Researchers often use acoustic data to study seabed geologic and geophysical processes, including those related to tectonics, seismicity, slope stability, and sediment transport. For example, seismic imaging of the seabed can be used to understand potential impacts on moored seabed infrastructure (e.g., windfarms) and instrumentation (e.g., cables). However, because seismic data is often only utilized on a local scale, fundamental inaccuracies remain in estimates of global seabed physical properties (e.g., sediment thickness and strength). Additionally, as new seismic data acquisition opportunities have become increasingly expensive and contentious endeavors, it is also becoming more critical to accurately predict geoacoustic properties in areas with sparse observational data (e.g., the Arctic). New methods for rescuing, aggregating, curating, analyzing, and interpreting marine geophysical data are therefore necessary to move toward improved understanding of the dynamic global seabed. Specifically, we are working to update and develop new methods to analyze large amounts of legacy marine seismic data, including multi-channel seismic reflection (MCS) and ocean-bottom seismometer (OBS) data. We aim to use these data to quantify fundamental properties like geologic layer thickness and velocity structure on regional to global scales. Similarly, we are using publicly available and newly collected marine geophysical data (e.g., Chirp, multibeam) to develop data-driven and process-based predictive models of environmental parameters related to seabed slope stability. We seek applicants who will creatively utilize publicly available geophysical data, especially marine seismic data, to improve upon traditional processing/interpretation workflows and add to the global database of seabed physical property observations (e.g., sound speed, density, layer thickness). We also welcome applicants seeking to improve process-based understanding of seabed stability and strength, especially those that utilize marine geophysical data and/or data-driven techniques (e.g., machine learning). Applicants will be expected to have expertise working with different types of marine geophysical data and a baseline understanding of global geologic/geophysical processes. Experience with geoacoustic data (e.g., Chirp, MCS, OBS) is strongly preferred. Applicants will also be expected to have some experience with common geospatial mapping software (e.g., QGIS, GMT) and/or programming languages (e.g., Python, MATLAB). Prior machine learning or modeling experience is ideal but not required.