The Machine Learning team of SimAI is looking for a promising research intern to focus on methodologies to estimate uncertainty of neural networks's predictions. As part of the project, the intern will have the opportunity to survey, implement and compare existing approaches, as well as develop new methodologies.

This internship provides a unique opportunity to study Deep Learning applied to Physics with a team of international experts.

As your main missions, you will:

• Collaborate closely with, and receive mentorship from the other members of the R&D team
• Study the state of the art of Uncertainty Estimation in Deep Learning
• Understand the deep learning models and the uncertainty quantification methods used in production
• Propose and implement new adapted methods to quantify the uncertainty of the predictions delivered by the deep learning models
• Evaluate and compare these methods on simple and industrial use cases

This position is primarily based in Paris, France.
One or two remote working days are possible

Context

Deep Learning has opened new possibilities for physical simulations, such as approximating the functional space of Partial Differential Equations (PDEs). However, traditional neural networks provide deterministic predictions, offering only point estimates, as the connections between neural states are defined by scalar weights. This approach makes it difficult to measure the uncertainty of Deep Learning models' predictions. In physical simulations, uncertainty quantification is essential to help engineers make informed decisions under real-world constraints.

Minimum Education/Certification Requirements and Experience

• MSc year 2+ level in Machine Learning, Computer Science, or related field (MS intership in Engineering School or University)
• Good knowledge of Python and Linux environement
• Experience with Deep Learning frameworks (PyTorch, Tensorflow, etc.)
• Motivation to work at the intersection of Physics and AI
• Excellence in communication and technical writing
• Fluent in English (spoken and written)

Preferred Qualifications and Skills

• Good communication and interpersonal skills
• Fluency with Git and basic software engineering patterns
• Good knowledge of Physics
• Ability to work collaboratively in a geographically distributed team

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