"Postdoctoral research fellows in generative, multimodal AI, and seismic foundational models"

School: Faculty of Arts and Sciences

Department/Area: Earth and Planetary Sciences

Position Description

Join our dynamic research team at Harvard University and spearhead groundbreaking research at the intersection of generative AI, multimodal learning, and Earth sciences. We are seeking a highly motivated Postdoctoral Research Fellow to develop and apply innovative, data-driven models for seismology, with a focus on developing cutting-edge foundation models. This is an exceptional opportunity to contribute to significant scientific discoveries and push the boundaries of AI in Earth science applications.

We are looking for passionate and driven individuals with expertise in one or more of the following areas:

• Generative AI
• Agentic AI
• Graph Representation Learning and Modeling
• Foundation Models
• Large Language Models
• Multimodal Learning
• Forecasting Models

Basic Qualifications

• A Ph.D. or equivalent degree in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field.
• Demonstrated strong research skills, evidenced by high-quality publications in top-tier machine learning/AI conferences and/or leading scientific journals.
• Excellent programming skills and hands-on experience with leading machine learning frameworks (e.g., TensorFlow, PyTorch).
• Practical experience with cloud computing platforms (e.g., AWS, GCP, Azure).

Additional Qualifications

• Experience with multi-GPU model training and large-scale inference.
• Familiarity with modern AI environments and tools.
• Prior experience applying AI to seismology or related Earth science domains.

Contact Information

Corinne Engber
20 Oxford St.
Cambridge, MA 02138

Contact Email: cengber@fas.harvard.edu

Salary Range

Pay offered to the selected candidate is dependent on factors such as years of experience, training or qualification, field of scholarship, and accomplishments in the field.

Minimum Number of References Required: 3

Maximum Number of References Allowed: 3

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