Deep Learning for modelling of physical systems

03/09/2021 – 04/09/2021 all-day

Offre en lien avec l’Action/le Réseau : – — –/Doctorants

Laboratoire/Entreprise : LAMSADE
Durée : 3 years
Contact :
Date limite de publication : 2021-09-03

Contexte :
We invite applications for a fully-funded PhD position on the topic of
“Deep Learning for physical systems modelling”.

This is a 3-year position funded by the ANR project SPEED and it will
start next fall (as soon as possible). The whole project is a
collaboration between the IJLRA, the LAMSADE and the new LISN labs.
Frequent scientific discussions and meetings are planed with many
students working in this project.

The interaction between machine learning and Physics has recently
emerged as a new and important research area. Some illustrations are
simulations of complex physical systems with machine learning models,
or at the opposite, the introduction of numerical methods in machine

Sujet :
At the interfaces of AI and Physics, different tracks can be
explored depending on the skills of the candidate:

– Noisy, scarce and partial observations of physical systems.
– Training algorithm to enforce physical properties.
– Interaction between the machine learning model and the physical
– Dealing with chaoticity

The algorithm developments should be assessed in interesting physical
situations. The accent is put here on chaotic dynamical systems as a
paradigm of complex systems. In particular, leaving aside the pure
data-driven approach, it would be important to find out whether a
physical-informed approach can overcome some of the challenges raised
by a blind use of machine-learning.

Profil du candidat :
– Outstanding master’s degree (or an equivalent university degree) in
computer science or Physics and other related disciplines (as e.g. mathematics, information sciences, computer engineering, etc.).
– Proficiency in machine learning and data analysis

Formation et compétences requises :
– Fluency in spoken and written English is required.
– The knowledge of python and pytorch is welcome

Adresse d’emploi :
Dauphine Université