Position Summary
Dr. Ian McBrearty’s lab in the Department of Earth, Environmental and Planetary Sciences is looking to hire a Postdoctoral Research Associate in the field of Machine Learning & Geophysics.
The McBrearty Lab sits at the intersection of machine learning and physics, focusing on developing data-driven techniques for earthquake monitoring, processing data from large seismic networks, and developing neural-surrogate emulations of PDEs governing tectonic and volcanic processes.
The ideal candidate will hold a Ph.D. in a quantitative field with strong Python and deep learning skills to build data-driven tools for earthquake detection and geophysical forecasting. They will be responsible for developing graph neural networks (GNNs), advancing PDE emulation methods, publishing high-impact research, and utilizing high-performance computing resources. Review of applications begins September 1, 2026, and will continue until the position is filled. Informal inquiries can be sent to Dr. Ian McBrearty at im76@rice.edu.
Workplace Requirements:
On campus position: This position is exclusively on-site, necessitating all duties to be performed in-person in Houston, Texas. Per Rice policy 440, work arrangements may be subject to change.
*Exempt (salaried) positions under FLSA are not eligible for overtime.
This position is funded by a grant, soft and/or restricted funds. Continued employment is contingent on the renewal of funding.
Proposed Salary: $65,000
Essential Functions
Develops and deploys machine learning models (specifically graph neural networks) to process large, spatially irregular seismic datasets and advance neural-surrogate emulations of PDEs governing geophysical processes
Documents, analyzes, and maintains research data
Publishes and presents research findings
Supports project management and collaboration across institutions or disciplines
Performs all other duties as assigned
Required Qualifications and Skills
Ph.D. in Geophysics, Computer Science, Data Science, Applied Mathematics, or a related quantitative field
Strong Python programming skills and practical experience with deep learning frameworks (e.g., PyTorch, TensorFlow) for scientific data analysis
Excellent verbal and written communication skills, as well as oral presentation skills
Organization and time management skills
Knowledge of modern research methods, data collection, and analyses
Ability to write scholarly papers based on ongoing research in order to submit them to journals for publication
Able to work in a collaborative environment
Able to work independently and professionally with minimal supervision and direction
Preferred Qualifications
Experience with Graph Neural Networks (GNNs) or physics-informed machine learning (PINNs).
Background in seismological software packages (e.g., ObsPy) and large-scale, high-performance computing (HPC) data processing.
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