Postdoctoral Associate - Earth Environmental Planetary Sciences

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 [email protected].

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

• 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

• 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 super vision and direction

• 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.

Rice University HR | Benefits: https://knowledgecafe.rice.edu/benefits

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