Computational Postdoctoral Fellow For AI-driven Seismic Imaging
Phase2 Technology, Austin, TX, United States
Computational Postdoctoral Fellow For AI-driven Seismic Imaging
Hiring Department:
Bureau of Economic Geology
Location:
PICKLE RESEARCH CAMPUS (J.J. Pickle Research Campus, North Austin)
Weekly Scheduled Hours:
40
Employment Duration:
Expected to continue until Apr 09, 2027
Position Type:
Fixed-term, one year with potential continuation
Responsibilities
Perform research in AI-driven waveform inversion using diffusion models.
Investigate the affordability and feasibility of 3D diffusion models for seismic imaging.
Conduct 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.
Ph.D. obtained within three years of the date of hire.
Preferred Qualifications
Ph.D. from a computation‑intensive program (e.g., applied mathematics, scientific computing) 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 collaboration with the Oden Institute for Computational Engineering and Sciences and/or the Bureau of Economic Geology, or a 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 under standard office conditions.
Repetitive use of a keyboard at a workstation.
Equal Opportunity Employer The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws governing nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
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Bureau of Economic Geology
Location:
PICKLE RESEARCH CAMPUS (J.J. Pickle Research Campus, North Austin)
Weekly Scheduled Hours:
40
Employment Duration:
Expected to continue until Apr 09, 2027
Position Type:
Fixed-term, one year with potential continuation
Responsibilities
Perform research in AI-driven waveform inversion using diffusion models.
Investigate the affordability and feasibility of 3D diffusion models for seismic imaging.
Conduct 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.
Ph.D. obtained within three years of the date of hire.
Preferred Qualifications
Ph.D. from a computation‑intensive program (e.g., applied mathematics, scientific computing) 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 collaboration with the Oden Institute for Computational Engineering and Sciences and/or the Bureau of Economic Geology, or a 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 under standard office conditions.
Repetitive use of a keyboard at a workstation.
Equal Opportunity Employer The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws governing nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions.
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