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Sorbonne Université

PhD position in Engineering and Computer Science, Sorbonne Université, Paris, Fr 

Foundation Models for Physics-Aware Deep Learning

Contact : Patrick Gallinari, patrick.gallinari@sorbonne-universite.fr

Location: Sorbonne Université, Pierre et Marie Curie Campus, 4 Place Jussieu, Paris, Fr. Machine Learning and Information Access team.

Candidate profile: Master degree in computer science or applied mathematics, Engineering school.  Background and experience in machine learning. Good technical skills in programming.

How to apply: please send a cv, motivation letter, grades obtained in master, recommendation letters when possible to patrick.gallinari@sorbonne-universite.fr

Start date: October/November 2024 for three years

Note: The research topic is open and depending on the candidate profile could be oriented more on the theory or on the application side

Keywords: deep learning, physics-aware deep learning, fluid dynamics, AI4Science

Full description: https://pages.isir.upmc.fr/gallinari/open-positions/

Abstract: Physics-aware deep learning aims at investigating the potential of AI methods to advance scientific research for the modeling of complex natural phenomena. This is a fast-growing research topic with the potential to boost scientific progress and to change the way we develop research in a whole range of scientific domains. An area where this idea raises high hopes is the modeling of complex dynamics characterizing natural phenomena occurring in domains as diverse as climate science, earth science, biology, fluid dynamics. Despite significant advances, this remains an emerging topic that raises several open problems in machine learning and application domains. Among all the exploratory research directions, the idea of developing foundation models for learning from multiple physics is emerging as one of the fundamental challenges in this field. This PhD proposal is aimed at exploring different aspects of this new challenging topic. Two main challenges will be investigated: learning from multiple physics and generalization with few shot learning.

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Diffusion SCAI


Thèse : PHysics-based Learning for robUst fluid SIMulation
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Date limite de candidature : 31/05/2024

Thèse : Synthetic reacting flow generation from a non-dimensional elementary data-driven model
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Date limite de candidature : juillet 2024

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Endhoven University of Technology

PhD, Numerical Linear Algebra tools in Scientific Machine Learning

The Scientific Computing group at TU Eindhoven is recruiting for a 4 year fully-funded PhD position to on “Numerical linear algebra (NLA) tools for Scientific Machine Learning (SciML)”. The position will be supervised by Prof. Victorita Dolean and Dr. Michiel Hochstenbach and the student will join the Centre for Analysis, Scientific Computing and Applications within the Mathematics and Computer Science department. Candidates with a Master degree in Applied Mathematics or a connected field with a strong mathematical and computational background are encouraged to apply.

The PhD project will explore the use of NLA tools in SciML  for the solution of large scale problems stemming from the discretisation of partial differential equations. Interaction between the field of NLA and optimisation will be explored, with the purpose of designing faster, more robust and reliable methods. Prior knowledge of traditional machine learning is not mandatory, but the candidate should be open to work at the interface of different research fields.

Applications will be considered until the position is filled, with a priority given to applications received before the 19th of May. 

The ideal starting date would be before October 2024 but some flexibility is possible. Full information can be found at https://jobs.tue.nl/nl/vacature/phd-on-numerical-linear-algebra-tools-in-scientific-machine-learning-1071179.html and inquiries can be directed to v.dolean.maini@tue.nl