Penn State University

APPLICATION INSTRUCTIONS:
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<li>CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. Please do not apply here, apply internally through Workday.</li>
<li>CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.</li>
<li>If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants.</li>
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Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants.
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.
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.
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
Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional wellbeing. In addition to comprehensive medical, dental, and vision coverage, employees enjoy robust retirement plans. One of the standout benefits is the generous 75% tuition discount, available to employees as well as eligible spouses and children.
For more detailed information, please visit our Benefits Page. (Note: For Postdoctoral benefits, please see our Postdoctoral Benefits page.)
CAMPUS SECURITY CRIME STATISTICS
Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here.
EEO IS THE LAW
Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473.
Penn State is committed to and accountable for advancing equity, respect, and belonging. We embrace individual uniqueness, as well as a culture of belonging that supports equity initiatives, leverages the educational and institutional benefits of inclusion in society, and provides opportunities for engagement intended to help all members of the community thrive. We value belonging as a core strength and an essential element of the university’s teaching, research, and service mission.
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Assistant Research Professor – Distributed Acoustic Sensing and Machine Learning

Assistant Research Professor – Distributed Acoustic Sensing and Machine Learning

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