Rice University is seeking to enhance community resilience and situational awareness during hurricane events by developing AI/ML-driven tools that leverage real-time data and physics-based models for risk and resilience estimation.
Requirements
- Strong proficiency in scientific programming and software development (e.g., Python, C++, Julia).
- Applied experience with AI/ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Ability to integrate and analyze diverse data sources, including geospatial and sensor data.
- Solid foundation in probabilistic risk assessment and resilience modeling.
- Experience with AI/ML frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, or Hugging Face.
- Knowledge of geospatial data processing and integration of diverse data sources (e.g., sensor data, remote sensing, social media, weather forecasts).
- Familiarity with deploying AI/ML or decision-support tools in cloud or high-performance computing environments.
Responsibilities
- Design, implement, and test algorithms for real-time situational awareness and risk/resilience estimation during hurricanes.
- Develop, document, and maintain software tools and codebases that integrate multi-modal data (e.g., sensor data, remote sensing, social media, model outputs).
- Apply and adapt modern AI/ML methods to hazard and infrastructure resilience problems.
- Deploy models and tools in high-performance or cloud computing environments to support near-real-time decision-making.
- Conduct research at the intersection of AI/ML, probabilistic risk assessment, and resilience modeling.
- Collaborate with a multi-disciplinary, multi-institutional research team, and engage with community partners to co-design solutions.
- Publish research results in high-impact journals and present findings at conferences.
Other
- Ph.D. in Civil Engineering, Computer Science, Systems Engineering, or a related field
- Effective written and verbal communication skills for technical reporting and collaborative engagement.
- Capacity to work independently while contributing to multi-disciplinary research teams.
- Demonstrated ability to develop reproducible, modular, and well-documented codebases, including version control (e.g., Git/GitHub) and collaborative development workflows.
- Prior experience mentoring students or coordinating work across interdisciplinary teams
