Assistant Research Professor – Distributed Acoustic Sensing and Machine Learning

The Pennsylvania State University; Ferguson Township (PA); On-site; USD 90,000 - 160,000; Full time

Benefits offered by this job

Tuition discount 75%

Comprehensive benefits package

Strong retirement plans

Job summary

Penn State's Department of Geosciences seeks a Research Assistant Professor to advance machine-learning methods for seismic source characterization with distributed acoustic sensing (DAS). The role includes wavefield simulations, synthetic DAS data, deep learning for event detection and location, and moment-tensor estimation, plus publications and mentoring.

The appointment is a term position with renewal based on funding and performance; collaboration with university and national-laboratory

Qualifications

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

Responsibilities

• Conduct physics-based wavefield simulations and generate synthetic DAS datasets.
• Develop 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, and mentoring of students.

Skills

DAS

Seismic modeling

Moment-tensor inversion

Machine learning

Scientific programming

High-performance computing

Education

Ph.D. in geophysics / seismology / related field

Tools

DAS software

HPC

Job description

POSITION SPECIFICS Research Assistant Professor – Distributed Acoustic Sensing and Machine Learning

The Department of Geosciences at Penn State seeks a Research Assistant Professor to support a project developing machine-learning methods for seismic source characterization using distributed acoustic sensing (DAS). The successful candidate will conduct physics-based wavefield simulations, generate synthetic DAS datasets, develop deep-learning models for seismic event detection, location, and full moment-tensor estimation, and validate these methods using field observations from earthquakes and controlled-source experiments. The position will also contribute to publications, project reports, student mentoring, and collaborations with university and national-laboratory partners.

This is a term position; length of the term will be discussed during the interview process. Continuation past the term length discussed will be based on university need, performance, and/or availability of funding.

Requirements

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

Background Checks/Clearances

Employment with the University will require successful completion of background check(s) in accordance with University policies.

Benefits

• Competitive benefits package for full-time employees designed to support both personal and professional wellbeing.
• Comprehensive medical, dental, and vision coverage.
• Robust retirement plans.
• Generous 75% tuition discount, available to employees as well as eligible spouses and children.

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EEO IS THE LAW

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