Assistant Research Professor – Distributed Acoustic Sensing and Machine Learning
Core
Develop machine-learning methods for seismic source characterization using distributed acoustic sensing (DAS), including physics-based wavefield simulations, synthetic dataset generation, and deep-learning models for event detection and moment-tensor estimation.
Role type
Research Assistant Professor (Machine Learning & Seismology)
Builds
Production ML models for seismic analysis, synthetic datasets, and scientific publications
Domain
Geophysics / Seismology / Machine Learning
Deliverable
production ML models
Required skills
DAS, seismic modeling, moment-tensor inversion, deep learning, scientific programming, high-performance computing
Responsibilities
Conduct physics-based wavefield simulations; generate synthetic DAS datasets; develop deep-learning models for seismic event detection, location, and full moment-tensor estimation; validate methods using field observations; contribute to publications and project reports; mentor students; collaborate with university and national-laboratory partners.