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
Fully funded masters internship with a PhD position mentioned. The post explicitly says fully funded, but no stipend amount, tuition coverage, or other financial details are provided.
Deadline
Mar 1, 2027
Country
France
University
École nationale des ponts et chaussées

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About this position
Fully funded opportunity in physics-constrained generative AI modelling at École Nationale des Ponts et Chaussées (ENPC) and Université Paris-Saclay, France, with work in CEREA, ENPC / Institut Polytechnique de Paris in collaboration with LISN (ANR MilaDy Project).
This post advertises a Master’s internship and a PhD position for candidates interested in computer science, physics, applied mathematics, and related areas. The research focus is on generative AI, machine learning, physical modelling, and fluid mechanics.
Eligibility highlights: Master 2 or final-year engineering student in Applied Mathematics, Computer Science, Physics, or equivalent. Strong Python skills and familiarity with ML frameworks such as PyTorch are required, along with interest in physical modelling or fluid mechanics.
Funding: The opportunity is described as fully funded. No stipend amount or tuition details are given in the post.
Deadline: Rolling basis until filled. The post also notes a start in spring 2027.
Interested applicants should follow the application route indicated in the post and apply as soon as possible because review is on a rolling basis.
Funding details
Fully funded masters internship with a PhD position mentioned. The post explicitly says fully funded, but no stipend amount, tuition coverage, or other financial details are provided.
What's required
Open to Master 2 or final-year engineering students in Applied Mathematics, Computer Science, Physics, or equivalent. Applicants should have solid Python skills and experience with machine learning frameworks such as PyTorch. Interest in physical modelling or fluid mechanics is preferred.
How to apply
Apply now through the linked post/application route. The announcement says applications are reviewed on a rolling basis until the position is filled.
More information can be found here
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