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CREST (ENSAE Paris)

Postdoctoral Position in Scientific Machine Learning for PDEs on Wasserstein Space (Finance and Insurance) CREST (ENSAE Paris) in France

Degree Level

Postdoc

Field of study

Computer Science

Funding

Postdoctoral position funded by the SLOPE project under the Hi!PARIS AI Cluster. Duration is 18 months with possible extension. Start date is as soon as possible. No stipend amount is stated.

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Country

France

University

CREST (ENSAE Paris)

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Keywords

Computer Science
Mathematics
Finance
Numerical Analysis
Quantitative Finance
Economics
Statistics

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About this position

Postdoctoral position in scientific machine learning for PDEs on Wasserstein space at CREST (ENSAE Paris) in Palaiseau, France.

The project is part of the SLOPE project funded by the Hi!PARIS AI Cluster and sits at the intersection of mathematics, computer science, economics, and finance. The successful candidate will work with Prof. Jean-François Chassagneux on machine-learning methods for nonlinear PDEs on Wasserstein space, including physics-informed neural networks (PINNs), structure-preserving learning methods, neural architectures, and neural operators.

Research topics include HJB and transport equations, representations and sampling of probability measures, stability, consistency, solution selection, and convergence. Applications are expected in mean-field control, mean-field games, and quantitative finance, with opportunities for publications and scientific dissemination.

This is a postdoctoral opening for an initial duration of 18 months with possible extension. The start date is as soon as possible. Applications are reviewed on a rolling basis until the position is filled.

To apply, send a CV including a list of publications, a short research statement, and the contact details of two academic referees to [email protected].

Funding details

Postdoctoral position funded by the SLOPE project under the Hi!PARIS AI Cluster. Duration is 18 months with possible extension. Start date is as soon as possible. No stipend amount is stated.

What's required

Applicants should have a strong background relevant to scientific machine learning, PDEs, numerical analysis, and/or quantitative finance. The post asks for a CV with list of publications, a short research statement, and contact details of two academic referees. Interest or experience in PINNs, structure-preserving learning methods, neural operators, mean-field control, or mean-field games would be relevant.

How to apply

Send a curriculum vitae, publication list, short research statement, and contact details of two academic referees to Prof. Jean-François Chassagneux by email. Applications are reviewed on a rolling basis until the position is filled.

More information can be found here

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