About the Role
The intern will contribute to research efforts in uncertainty quantification, robust design optimization, and the development of surrogate models for complex systems. Work will involve algorithm development, computational experiments, and integration of statistical and machine learning techniques to support reliable design under uncertainty.
Responsibilities
- Develop and test algorithms for uncertainty propagation in high-dimensional systems
- Implement and compare methods for global sensitivity analysis
- Construct and validate surrogate models such as Gaussian processes and polynomial chaos expansions
- Optimize system designs under uncertainty using robust and reliability-based approaches
- Analyze performance of models under noisy or incomplete data conditions
- Collaborate on integrating probabilistic reasoning into design workflows
- Evaluate trade-offs between model fidelity and computational cost
- Support validation of uncertainty-aware decision-making pipelines
- Document methodologies and results for internal review and reproducibility
- Present technical findings to research and engineering teams
Compensation
Paid hourly
Work Arrangement
Hybrid
Team
Research and development team focused on foundational methods for trustworthy AI in engineering applications
Application Process
Interested candidates should submit a resume, a brief statement of research interests, and a transcript or list of relevant coursework. Applications will be reviewed on a rolling basis until the position is filled.
Duration
This is a part-time or full-time internship lasting 10 to 12 weeks, with flexible start dates depending on candidate availability.
Not available
