Interpretable and Robust Machine Learning for Physics-Based Numerical Simulations

PhD position on statistical methods to improve interpretability and robustness of machine learning models used to accelerate physics simulations.

Responsibilities

• Develop statistical methods to enhance interpretability and robustness of ML models used in accelerating physics simulations.

Requirements

• Master's degree in Applied Mathematics, Statistics, Computer Science, or related field.
• Strong background in statistics, machine learning, or scientific computing.
• Programming skills in Python, R, or similar.
• Interest in physics-based simulations and uncertainty quantification.
• Fluency in English.