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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Post-doctoral Position in Machine Learning for Subsurface Multiscale Structure and Characterization including Permeability at the University of Pittsburgh - **Company:** University of Pittsburgh - **Location:** Pittsburgh, PA, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Artificial Neural Networks, Databases, Relational Databases, Python (Programming Language), Machine Learning, Tensorflow, Pytorch, Deep Learning, Convolutional Neural Networks, Core Data, Information Technology - **Published:** August 25, 2026 - **Apply:** https://www.disabledperson.com/jobs/74513700-post-doctoral-position-in-machine-learning-for-subsurface-multiscale-structure-and-characterization-including-permeability-at-the-university-of-pittsburgh ## About the Role * 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. ## Description 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. ## Related Videos - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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