[ML-news] Postdoc positions in Georgia Tech
Bo Dai
We have funded postdoc position in Machine Learning and World Model for Climate Science available in Georgia Tech. Please see the following for details.
Postdoctoral Research Position in Machine Learning, World Models, and Agentic AI for Climate Science
The School of Computational Science and Engineering (CSE) and the School of Earth and Atmospheric Sciences (EAS) at the Georgia Institute of Technology invite applications for a highly motivated postdoctoral researcher to develop next-generation world-modeling frameworks for Earth system science and climate dynamics. The successful candidate will work at the interface of physics-based simulation and data-driven learning, developing stochastic, multi-scale, and generative world models that approximate the conditional distribution of climate and hazard responses under uncertain atmospheric forcings, including tropical cyclone trajectories and intensities. These models will enable end-to-end mappings from uncertain climate drivers to high-resolution, spatially explicit impact fields, capturing compound and nonlinear interactions across coupled Earth system processes. The project will further advance agentic AI, reinforcement learning, and model-based decision-making over learned simulators, leveraging large-scale simulation ensembles to enable risk-aware and robust decision-making under uncertainty. This includes the development of intelligent agents that interact with learned environmental models to perform adaptation planning and optimization over high-dimensional intervention spaces, enabling systematic evaluation of diverse adaptation and mitigation strategies under deep climatic, environmental, and socio-economic uncertainty.
This position is embedded within ongoing interdisciplinary efforts under the Georgia Tech for Georgia’s Tomorrow (GT2) program. These initiatives integrate high-resolution numerical simulations, probabilistic forecasting systems, and machine-learning–based surrogates and emulators to bridge the longstanding gap between large-scale atmospheric prediction and fine-scale impact and adaptation assessment. The overarching vision is to enable computationally efficient, probabilistic world representations of coastal hazards and resilience systems that support actionable, equity-informed adaptation planning.
For additional information, please visit:
Qualifications
- Ph.D. in Computer Science, Computational Science and Engineering, Interactive Computing, Computational Physics, Earth System Science, or a closely related field.
- Demonstrated experience in one or more of the following areas:
- World Modeling, Reinforcement Learning and Agentic AI, (including LLMs), for physics-informed modeling, optimization, or surrogate/emulator development.
- Computational physics, numerical hydrodynamic modeling, or climate hazard modeling.
- Strong programming and computational skills (e.g., Python, HPC workflows, scientific computing).
- Excellent written and oral communication skills.
Experience in developing or applying world models and agentic AI systems, including model-based decision-making over learned simulators and translating these into actionable strategies, is strongly preferred.
Position Description
- Appointment: Initial term of one year (renewable up to two years based on performance and funding).
- Anticipated Start Date: Summer 2026 (flexible).
- Mentors: Dr. Ali Sarhadi, Dr. Bo Dai (CSE), and Dr. Alex Robel (EAS), Georgia Tech.
Salary
This position offers an annual salary of $70,000, along with a comprehensive benefits package.
Application Instructions
To apply, please send a single PDF to Dr. Ali Sarhadi at sar...@gatech.edu containing the following:
- A cover letter detailing your interest in the position and qualifications.
- A curriculum vitae (CV).
- Contact information for two to three professional references.
- (Optional) A one- to two-page statement describing your research interests.
Prospective applicants are encouraged to contact Dr. Sarhadi and Dr Bo Dai (bo...@cc.gatech.edu) with any questions prior to submitting an application.
About the Schools of Computational Science and Engineering and Earth and Atmospheric Sciences
The School of CSE and EAS at Georgia Tech are highly interdisciplinary, spanning computational science, machine learning, AI, and data-driven modeling, as well as atmospheric, oceanic, planetary, and Earth system sciences. Together, they foster integrative research at the intersection of advanced computation, AI, and climate science. For more information, visit cse.gatech.edu and eas.gatech.edu.