Stanford, Postdoc: Data-driven Prediction for Subsurface Flow

Postdoc Data Scientist position at Stanford Dept of Energy Resources Engineering to develop data-driven prediction modeling for subsurface flow.

Stanford, Department of ERE At: Stanford, Department of ERE
Location: Stanford, CA

Postdoc Position: Data-driven Prediction and Uncertainty Reduction for Subsurface Flow Applications

Department of Energy Resources Engineering, Stanford University

The Stanford Smart Fields Consortium, an interdisciplinary affiliates program within the Department of Energy Resources Engineering, is seeking a data scientist to develop data-driven modeling procedures for subsurface flow. Flow modeling and prediction are traditionally accomplished using PDE-based simulators.

The goal of this research is to explore how production and other data can be used for performance prediction, uncertainty reduction, and optimization in oil and gas fields. The project will involve interaction with other researchers in the Stanford Smart Fields Consortium and at Chevron Energy Technology Company.

Existing consortium projects include optimization of oil field and geological carbon storage operations, optimization under uncertainty, solution of inverse problems, and reduced-order modeling (for a more complete list of projects, see

The candidate must have a Ph.D. degree in data science, applied mathematics, engineering, computer sciences, or a closely related field. Previous experience with subsurface flow modeling is not required.

Interested applicants should send (1) a curriculum vitae including a complete list of publications, (2) names of three references including email addresses, and (3) pdfs/links for up to three relevant papers to:

Ms. Thuy Nguyen
Department of Energy Resources Engineering,
Stanford University

This search will proceed until a suitable candidate is selected.

Please see for information on the Department of Energy Resources Engineering at Stanford.

Stanford University has a strong institutional commitment to the principle of diversity. In that spirit, we particularly encourage applications from women, members of ethnic minorities, and individuals with disabilities.