Interpretable and Robust Machine Learning for Physics-Based Numerical Simulations

Edtlab; France; On-site; EUR 25,000 - 35,000; Full time

Job summary

Edtlab is offering a PhD position focused on developing statistical methods to improve the interpretability and robustness of machine learning models. This role is vital in advancing physics simulations.

The ideal candidate should hold a Master's degree in Applied Mathematics, Statistics, or Computer Science. A strong background in statistics and machine learning is essential, along with programming skills in Python or R. Fluency in English is required.

Qualifications

• 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.

Responsibilities

• Develop statistical methods to enhance interpretability and robustness of ML models.

Skills

Statistics

Machine learning

Scientific computing

Programming in Python

Programming in R

Uncertainty quantification

Education

Master's degree in Applied Mathematics

Master's degree in Statistics

Master's degree in Computer Science

Job description

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.