Uncertainty Quantification for Surrogate Models Postdoctoral Researcher
Core
Conduct basic research and verification/validation of uncertainty quantification (UQ) methods for AI surrogate models, specifically focusing on Deep and Scalable Gaussian processes.
Role type
Postdoctoral Researcher (IC)
Builds
Specialized analysis software tools and models for UQ metrics
Domain
Applied Mathematics / Machine Learning / National Security
Deliverable
production ML models
Required skills
Deep Gaussian processes, Scalable Gaussian processes, Functional data analysis, AI surrogates (neural networks), UQ methods, Python/R/Matlab, TensorFlow/PyTorch/JAX, Peer-reviewed publication experience
Preferred skills
Active learning/sequential design, Splines, High-performance computing (MPI)
Technologies
TensorFlow, PyTorch, JAX, MPI
Responsibilities
Conduct basic research in efficient Gaussian processes, Collaborate with multidisciplinary teams, Develop and validate analysis software, Publish research results in journals and present at conferences
Seniority
Postdoctoral, independent researcher
