Job Description

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 [contact details available in the full listing].

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
  
  

Job Description

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 [contact details available in the full listing].

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