Computational Postdoctoral Fellow For AI-driven Seismic Imaging Full-time NEW

Computational Postdoctoral Fellow For AI-driven Seismic Imaging

This is a fixed-term position for one year, located at the J.J. Pickle Research Campus in North Austin.

The position will enhance the Bureau of Economic Geology's competency in computational seismology, specifically AI-driven full-waveform inversion using diffusion-model approaches.

Responsibilities include:

• Perform research in AI-driven waveform inversion using diffusion models.
• Investigating 3D diffusion model's affordability and possibility in seismic imaging.
• Perform research in joint seismic inversion for velocity and density models.

Required Qualifications

• Ph.D. in Geophysics, Applied Mathematics, Computational Science, or a closely related field, with demonstrated research experience in seismic imaging, waveform inversion, or inverse problems.
• Strong background in full waveform inversion and wave-equation–based modeling, including familiarity with adjoint-state methods, gradient-based optimization, and multi-scale inversion strategies.
• Proven expertise in machine learning and deep learning for scientific applications, including hands-on experience with neural network architectures (e.g., CNNs, autoencoders, physics-informed or hybrid models) and modern frameworks such as PyTorch or TensorFlow.
• Advanced programming and high-performance computing skills, including proficiency in Python and/or C/C++, experience with GPU acceleration, and the ability to develop, test, and maintain research-grade software for large-scale numerical experiments.
• PhD must have been obtained within 3 years from date of hire.

Preferred Qualifications

• Ph.D. degree from a computation-intensive program, such as applied mathematics, scientific computing, or a closely related field, with training that emphasizes large-scale numerical modeling and high-performance computing.
• Demonstrated research experience with advanced generative AI methods, particularly modern probabilistic or generative frameworks such as denoising diffusion probabilistic models (DDPMs), score-based models, or related approaches, ideally applied to inverse problems or physical sciences.
• Prior academic or research affiliation with the University of Texas at Austin, including close collaboration with the Oden Institute for Computational Engineering and Sciences and/or the Bureau of Economic Geology, or an established record of collaborative research with these institutions.
• Strong interdisciplinary collaboration experience, particularly at the interface of seismic inversion, computational science, and machine learning, with evidence of independently leading or co-leading research projects.

Salary Range

$65,000

Working Conditions

• May work around standard office conditions
• Repetitive use of a keyboard at a workstation

Required Materials

• Resume/CV
• 3 work references with their contact information
• Letter of interest