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École nationale des ponts et chaussées

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Fully Funded Masters Internship and PhD Position in Physics-Constrained Generative AI Modelling École Nationale des Ponts et Chaussées in France

Degree Level

Master's, PhD

Field of study

Computer Science

Funding

The post states this is a fully funded masters internship with a PhD position. No stipend amount, tuition coverage details, or other financial terms are provided.

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Country

France

University

École nationale des ponts et chaussées

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Keywords

Computer Science
Mechanical Engineering
Mathematics
Fluid Mechanics
Python Programming
Statistics
Physics
Applied Mathematic
ML

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About this position

Fully funded masters internship with a PhD position in physics-constrained generative AI modelling at École Nationale des Ponts et Chaussées (ENPC) and Université Paris-Saclay, France.

The project is hosted at CEREA, ENPC / Institut Polytechnique de Paris, in collaboration with LISN under the ANR MilaDy Project. It is aimed at students interested in the intersection of computer science, applied mathematics, physics, and fluid mechanics, with a strong emphasis on machine learning and physical modelling.

Eligibility highlights: Master 2 students or final-year engineering students in Applied Mathematics, Computer Science, Physics, or an equivalent background. Applicants should have solid Python skills and be comfortable with machine learning frameworks such as PyTorch. Interest in physical modelling or fluid mechanics is specifically mentioned.

Funding: The post is described as fully funded. No stipend amount or detailed funding breakdown is given.

Application: The call is on a rolling basis until the position is filled. The post says “Apply now,” but no separate application portal is provided in the text.

Research keywords: physics-constrained generative AI, machine learning, applied mathematics, computer science, physics, fluid mechanics, Python, PyTorch.

Funding details

The post states this is a fully funded masters internship with a PhD position. No stipend amount, tuition coverage details, or other financial terms are provided.

What's required

Applicants should be Master 2 students or final-year engineering students in Applied Mathematics, Computer Science, Physics, or an equivalent field. Strong Python skills and experience with machine learning frameworks such as PyTorch are required. Interest in physical modelling or fluid mechanics is preferred.

How to apply

Apply now through the opportunity linked in the post. The call is on a rolling basis until the position is filled.

More information can be found here

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