Seismic DAS & ML Research Professor — Wavefield & AI

Penn State University; University Park (TX); Sur place; USD 95 000 - 130 000; Plein temps

Avantages offerts par ce poste

75% tuition discount

Résumé du poste

Penn State University in University Park, PA, seeks a Research Assistant Professor to advance machine-learning methods for seismic source characterization using distributed acoustic sensing. You will run physics-based simulations, generate synthetic DAS data, and develop deep-learning models for event detection, location, and moment-tensor estimation.

The role involves publishing results, mentoring students, and collaborating with university and national-lab partners, with a term-length to be

Qualifications

• Ph.D. in geophysics, seismology, applied mathematics, computer science, or a related field by date of hire.
• Experience in DAS, seismic modeling, moment-tensor inversion, machine learning, scientific programming, and high-performance computing is highly desirable.

Responsabilités

• Develop physics-based wavefield simulations and generate synthetic DAS datasets.
• Create deep-learning models for seismic event detection, location, and full moment-tensor estimation.
• Validate methods using field observations from earthquakes and controlled-source experiments.
• Contribute to publications, project reports, student mentoring, and collaborations with partners.

Connaissances

DAS

Seismic modeling

Moment-tensor inversion

Machine learning

Scientific programming

High-performance computing

Formation

Ph.D. in geophysics, seismology, applied mathematics, computer science, or related field

Description du poste

Penn State University in University Park, PA, seeks a Research Assistant Professor to advance machine-learning methods for seismic source characterization using distributed acoustic sensing. You will run physics-based simulations, generate synthetic DAS data, and develop deep-learning models for event detection, location, and moment-tensor estimation.

The role involves publishing results, mentoring students, and collaborating with university and national-lab partners, with a term-length to be