Post-doctoral Position in Machine Learning for Subsurface Multiscale Structure and Characterization including Permeability at the University of Pittsburgh in Pittsburgh, Pennsylvania at University of Pittsburgh

Post-doctoral Position in Machine Learning for Subsurface Multiscale Structure and Characterization including Permeability at the University of Pittsburgh
Geology and Environmental Sciences - Pennsylvania-Pittsburgh - (26005201)

The University of Pittsburgh is seeking a highly motivated and creative Postdoctoral Researcher to join a cutting-edge project focused on applying artificial intelligence and machine learning (AI/ML) to critical subsurface energy challenges for a three-year post-doctoral appointment. This position is funded by the United States Department of Energy Science-informed Machine Learning to Accelerate Real Time subsurface decision making (SMART) LDRD Prime initiative.

The successful candidate will be central to a project aiming to develop a breakthrough, laboratory-calibrated AI/ML tool that accurately estimates subsurface permeability from commonly collected geophysical well logs. This research will address a key challenge in subsurface characterization for applications including energy resources, production efficiency, and recovery optimization. The researcher will work with a multidisciplinary team to build, train, and validate novel deep learning models, leveraging unique datasets from national laboratories.

Key Responsibilities

The postdoctoral researcher will be integral to achieving the project's ambitious goals and will be expected to:

• Demonstrated experience with developing relational databases and database schemas.

• Lead the construction of a fully attributed, machine learning-ready petrophysical database from existing NETL ultrasonic and core measurement archives.
• Develop, train, and deploy deep learning models, including convolutional neural networks (CNNs) and physics-informed neural networks (PINN), to predict rock permeability from ultrasonic acoustic measurements.
• Adapt and retrain existing deep learning frameworks (e.g., PhaseNet) to automate the picking of P and S wave arrivals from ultrasonic waveform data, enhancing the speed and consistency of laboratory analysis.
• Develop and apply generative adversarial networks (GANs) to produce realistic synthetic core data, broadening the training datasets for more robust AI/ML models.
• Integrate and validate the developed models by applying them to existing wireline log data and potentially new core samples.
• Collaborate closely with NETL scientists and researchers in geophysics, geology, engineering, and computer science.
• Publish research findings in high-impact, peer-reviewed journals and present results at major scientific conferences.

Required Qualifications

• A Ph.D. in Geophysics, Geology, Petroleum Engineering, Computer Science, or a closely related field. The degree must have been completed within the last five years from the start date of the appointment.
• Must be a United States Citizen.
• Demonstrated experience in applying machine learning or deep learning techniques to scientific problems.
• Proficiency in scientific programming with Python and experience with common ML/DL libraries (e.g., TensorFlow, PyTorch).
• Strong analytical and problem-solving skills.
• Excellent written and oral communication skills, with a demonstrated ability to work both independently and as part of a collaborative team.

Preferred Qualifications

• Experience working with geophysical, petrophysical, or well log datasets.
• A strong background in rock physics, acoustics, or seismic data analysis.
• Specific experience with advanced neural network architectures such as CNNs, PINNs, or GANs.
• A track record of scholarly achievement, including first-author publications in peer-reviewed journals.
• Familiarity with high-performance computing environments.

Required:

• A Ph.D. in Geophysics, Geology, Petroleum Engineering, Computer Science, or a closely related field. The degree must have been completed within the last five years from the start date of the appointment.

Preferred:

• Experience working with geophysical, petrophysical, or well log datasets.

• A strong background in rock physics, acoustics, or seismic data analysis.

• Specific experience with advanced neural network architectures such as CNNs, PINNs, or GANs.

• A track record of scholarly achievement, including first-author publications in peer-reviewed journals.

• Familiarity with high-performance computing environments.

To apply: Please apply within the Talent Center job posting through join.pitt.edu, and email Dr. William Harbert at harbert@pitt.edu with email subject “Postdoc Opportunity” and the following combined as 1 pdf attachment: A cover letter describing your career goals, experience, and interest in the position; Resume/CV including contact information for three professional references.

The University of Pittsburgh is an equal opportunity employer.

The University of Pittsburgh is an equal opportunity employer / disability / veteran.

Assignment Category: Full-time regular
Campus: Pittsburgh
Child Protection Clearances: Not Applicable
Required Attachments: Cover Letter, Curriculum Vitae

Assignment Category Full-time regular