Postdoctoral Research Associate in Stochastic Sensitivity Analysis for Multi-Physics Problems

About the position

The Postdoctoral Research Associate position at Los Alamos National Laboratory focuses on developing and implementing stochastic sensitivity analysis and uncertainty quantification methods for multi-physics problems. The role involves collaboration with a multidisciplinary team to enhance computational physics research, particularly in areas such as machine learning, applied mathematics, and numerical methods. The successful candidate will contribute to significant projects and publish findings in scientific literature.

Responsibilities

• Develop and implement novel stochastic sensitivity analysis methods for multi-physics models.
• Create uncertainty quantification tools to estimate the propagation of probability distributions in complex models.
• Collaborate with scientists in various fields including numerical methods, data science, and experimental design.
• Contribute to LANL projects and produce publishable results for scientific literature and technical conferences.

Requirements

• PhD in applied mathematics, physics, engineering, or related fields, completed within the last five years or soon to be completed.
• Experience and strong track record in sensitivity analysis methods, such as adjoint sensitivity.
• Proficient in machine learning and AI models.
• Proficient in statistical methods and probability theory.
• Programming experience, preferably in Python, Fortran, or C/C++.
• Excellent oral and written communication skills.

Nice-to-haves

• Experience using AI models for probability distributions, such as diffusion models and generative models.
• Experience with adjoint modeling and/or automatic differentiation tools.
• Knowledge of Koopman and Perron-Frobenius operator theory.
• Experience with deep learning and tensor libraries such as PyTorch.
• Ability to meet tight schedules and prioritize tasks effectively.

Benefits

• Competitive salary based on PhD completion date.
• Opportunities for collaboration and publication in scientific literature.