Texas A&M University seeks to solve problems related to planetary evolution and habitability by integrating advanced machine learning techniques with mantle convection modeling in geodynamics.
Requirements
- Ph.D. (or completed all requirements by appointment) in a relevant field such as Earth and planetary sciences, Applied and Computational Mathematics, Physics, Engineering, or related field.
- Strong background in numerical skills and machine learning.
- Expertise in computational geodynamics modeling.
- Familiarity with theoretical and practical aspects of scientific machine learning.
- Prior experience with neural operators or Physics-Informed Neural Networks (PINNs) in computational fluid dynamics is an advantage.
- Advanced scientific computing and math skills.
- Excellent writing skills through high-quality reports and publications.
Responsibilities
- Develops neural operator and physics-based machine learning models for mantle convection system.
- Analyzes, interprets, and publishes modeled results.
- Mentors graduate and undergraduate students in the lab.
- Attends and presents results at scientific conferences.
- Collaborates with Professor Qian Yuan on integrating machine learning techniques with geodynamics.
- Uses state-of-the-art AI resources such as the NVIDIA DGX SuperPOD for computational modeling.
- Conducts research in AI-driven geodynamics to investigate planetary evolution and habitability.
Other
- Ability to sit for extended periods (e.g., 6-8 hours/day).
- Visual acuity to read computer screens and documents.
- Ability to multitask and work cooperatively with others.
- Excellent communication and presentation skills.
- Ability to positively and professionally interact with clients and staff, groups of various sizes.
