Postdoctoral Associate - Earth Environmental Planetary Sciences

Rice University • Houston, TX

1d • Onsite

About The Position

The MUSE (Multimodal AI for Earth System Discovery) research cluster at Rice University is seeking a Postdoctoral Research Associate to develop multi-domain graph neural networks (GNNs) and foundation models for Earth system modeling, state estimation, and forecasting. The research will address irregular, multi-scale, and heterogeneous observations across Earth science domains, including hydrology, floods, cryosphere processes, and earthquakes. The role focuses on reusable, scale-adaptive, graph-based foundation architectures and transferable, physics-informed models that infer hidden Earth states, identify governing relationships, and quantify predictive uncertainties.

The appointment is for an initial one-year term, renewable for up to two to three years, subject to performance and funding availability. The position is exclusively on-site in Houston, Texas. It is grant-funded, and continued employment depends on renewal of funding. The role is exempt from overtime under the FLSA.

The postdoctoral associate will receive co-mentorship from faculty spanning machine learning, GNN theory, remote sensing, and geophysics; access to Rice high-performance computing clusters and specialized GPU nodes; a competitive salary and Rice University benefits package; and career guidance for academic or industry research pathways.

Requirements

• Ph.D. in Computer Science, Data Science, Geophysics or Earth Sciences, Applied Mathematics, Electrical Engineering, Environmental Engineering, or a related quantitative field.
• Strong background in deep learning on graphs (GNNs), geometric deep learning, or large-scale foundation model architectures, with an interest or background in applying these methods to physical or geoscientific systems.
• High proficiency in Python and deep learning frameworks such as PyTorch and PyTorch Geometric or DGL.
• Demonstrated track record of peer-reviewed publications in high-impact computational or geoscience journals and conferences.

Nice To Haves

• Experience working with spatiotemporal or irregularly sampled physical data, such as remote sensing, streamflow networks, seismic arrays, InSAR, or terrain data.
• Experience with Physics-Informed Neural Networks (PINNs), neural operators such as FNO or DeepONet, or neural surrogate modeling for physical processes.
• Background in uncertainty quantification or hybrid machine-learning frameworks combining data-driven models with physical priors.
• High-performance computing or cloud computing experience, including distributed training such as CUDA or multi-GPU training.

Responsibilities

• Design, build, and benchmark multi-domain graph neural network architectures and neural surrogates for complex spatial networks, irregular geometries, and spatiotemporal prediction tasks.
• Integrate multimodal, heterogeneous Earth observations, such as seismic networks, river graphs, satellite altimetry, and Distributed Acoustic Sensing, and evaluate transferability across Earth system domains.
• Incorporate physical constraints, including PDEs and mass and momentum conservation laws, into graph architectures and uncertainty quantification frameworks.
• Assist cluster faculty with interdisciplinary research proposals, including proposals to NSF, DOE, and NASA.
• Collaborate with researchers in Computer Science, Electrical and Computer Engineering, and Earth, Environmental and Planetary Sciences at Rice.
• Publish research in leading machine-learning venues and geoscience journals, and present at national and international conferences.

Benefits

• Competitive salary and full Rice University benefits package.
• Co-mentorship from a collaborative faculty team spanning machine learning, GNN theory, remote sensing, and geophysics.
• Access to Rice’s high-performance computing clusters and specialized GPU acceleration nodes.
• Dedicated mentorship and career guidance tailored toward academic or industry research pathways.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree