APPLICATION INSTRUCTIONS:

• 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.

• 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.

• If you are NOT a current employee or student, please click “Apply” and complete the application process for external applicants.

 

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

The College of EMS - Energy Institute at Penn State invites applications for an immediate position of Postdoctoral Scholar to conduct research on projects in collaboration with Dr. Sanjay Srinivasan, who directs the Penn State Initiative for Geostatistics and Geo-Modeling Applications (PSIGGMA). 

This initiative currently supports a group of 5 researchers working on topics such as the application of reinforcement learning for optimum reservoir development, the application of machine learning and multipoint geostatistics for characterization of fractures and novel algorithms for the integration of time-lapse seismic data into models for CO2 plume movement during sequestration.

These projects are supported through grants from NSF, DOE and the John and Willie Leone Family Endowment.

Applications are sought from researchers working in the areas of advanced data analytics and machine learning applied to solve subsurface reservoir characterization and modeling related challenges. Specifically, expertise looking at geochemical, geomechanical, and hydrologic data sets, high-performance modeling capabilities and the development of a suite of AI technologies, including surrogate models, physics-informed machine learning, and digital twins to enhance engineering evaluation and control of the subsurface during characterization, drilling,
stimulation, and/or production will be preferred

Applicants must hold an advanced degree, Ph.D. or equivalent in petroleum/subsurface engineering, geophysics, AI/ML, geostatistics or related field by hire date.

Strong background and training in reservoir characterization techniques and/or subsurface process modeling especially using advanced data analytics and machine learning approaches is required. 

Candidates should possess excellent written and verbal communication skills, be able to work independently and have excellent computer skills. Clear demonstration of computer coding skills and use of data analysis software is desirable. 

Interested candidates should submit the following:

• An applic