Postdoctoral Associate - Earth Environmental Planetary Sciences

Postdoctoral Research Associate - Multimodal & Graph AI for Earth Systems

Position Summary

The MUSE (Multimodal AI for Earth System Discovery) research cluster at Rice University-led by a cross-departmental faculty team (Drs. Noemi Vergopolan, Arlei Silva, Ian McBrearty and Andrew Hoffman) and associates (Sylvia Dee, Avantika Gori, Jonathan Ajo-Franklin, and Guha Balakrishnan)-is seeking a Postdoctoral Research Associate to pioneer multi-domain Graph Neural Networks (GNNs) and Foundation Models for Earth system modeling, state estimation, and forecasting.

Earth observations are inherently irregular, multi-scale, and heterogeneous-ranging from river networks and seismic stations to InSAR satellite data and subglacial sensors. The goal of this position is to develop reusable, scale-adaptive, graph-based foundation architectures capable of learning unified representations across diverse Earth science testbeds, including hydrology, flood dynamics, ice-sheet/aquifer mechanics, and earthquake processes. Rather than treating AI as a black box, this role focuses on developing transferable, physics-informed models that infer hidden Earth states, discover governing relationships, and quantify predictive uncertainties across interconnected Earth domains.

The position is for an initial one-year term, renewable for up to two to three years, subject to performance and funding availability.

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.

Essential Functions

• GNN & Foundation Model Architecture Development: Design, build, and benchmark multi-domain graph neural network architectures and neural surrogates tailored for complex spatial networks, irregular geometries, and spatio-temporal prediction tasks.

• Cross-Domain Transfer Learning: Integrate multimodal, heterogeneous Earth observations (e.g., seismic networks, river graphs, satellite altimetry, Distributed Acoustic Sensing) to evaluate model transferability across distinct Earth system domains (Water, Floods, Cryosphere, Earthquakes).

• Physics-Aware Machine Learning & UQ: Incorporate physical constraints (e.g., PDEs, mass/momentum conservation laws) into graph architectures and uncertainty quantification (UQ) frameworks.

• Proposal Development & Cluster Collaboration: Assist cluster faculty with interdisciplinary research proposals (e.g., NSF, DOE, NASA) and work closely with collaborators across Computer Science, Electrical & Computer Engineering, and Earth, Environmental and Planetary Sciences (EEPS) at Rice.

• Dissemination: Publish high-impact research in top-tier machine learning venues (e.g., NeurIPS, ICML, ICLR) and geoscientific journals (e.g., AGU, Nature Geoscience) and present at national and international conferences.

Required Qualifications and Skills

• Ph.D. in Computer Science, Data Science, Geophysics / 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 (e.g., PyTorch, PyTorch Geometric / DGL).

• Demonstrated track record of peer-reviewed publications in high-impact computational or geoscience journals and conferences.

Preferred Qualifications

• Experience working with spatial-temporal or irregularly sampled physical data (e.g., remote sensing, streamflow networks, seismic arrays, InSAR, terrain data).

• Experience with Physics-Informed Neural Networks (PINNs), neural operators (e.g., FNO, DeepONet), or neural surrogate modeling for physical processes.

• Background in Uncertainty Quantification (UQ) or hybrid machine learning frameworks combining data-driven models with physical priors.

• High-performance computing (HPC) and/or cloud computing with expertise in distributed training (e.g., CUDA, multi-GPU training).

What We Offer

• Interdisciplinary Mentorship: Co-mentorship from a collaborative faculty team spanning ML, GNN theory, remote sensing, and geophysics.

• World-Class Computing: Full access to Rice's High-Performance Computing (HPC) clusters and specialized GPU acceleration nodes.

• Competitive Compensation: Competitive salary and full Rice University benefits package.

• Career Development: Dedicated mentorship and career guidance tailored toward pathways in academia or industry research labs.

To Apply

Please submit the following application materials via the Rice University Application Portal. Combine all required materials into a single PDF file and upload it under the section labeled "CV/Resume."

• Cover Letter (highlighting relevant research experience with GNNs, foundation models, or Earth science data)
• Curriculum Vitae (CV)
• Research Statement (1-2 pages detailing research accomplishments and future interests)
• Contact Information for 3 Professional References

Review of applications begins on December 1st, 2026, and will continue until the position is filled. Informal inquiries can be sent to Rice EEPS (esci-postdocs@rice.edu) or cluster faculty members.

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