Annonce en lien avec l’Action/le Réseau : aucun
Laboratoire/Entreprise : LIS / LAM
Durée : 3 years
Contact : François-Xavier Dupé francois-xavier.dupe@univ-amu.fr LIS and Annie Zavagno annie.zavagno@lam.fr LAM
Date limite de publication : 2018-04-10
Contexte :
The PhD subject has been pre-selected for funding by Aix Marseille Université (Doc2AMU) and is supported by EURANOVA Marseille.
For the PhD to be funded the selected candidate will have to defend the project in front of the selection commitee.
Please find more information at on the Doc2Amu website: https://doc2amu.univ-amu.fr/en/bigsf
Sujet :
We are seeking for a PhD candidate interested to apply machine learning methods on large astrophysical datasets. The main challenge is to develop new tools to study star formation in the Galaxy. Such tools include classical ML methods, but also deep learning methods suited to the Big Data-Class databases used. Moreover, data come from different instruments, requiring to manage heterogeneous data in a multi dimensional space.
Before applying please contact the PhD supervisors: François-Xavier Dupé francois-xavier.dupe@univ-amu.fr LIS and Annie Zavagno annie.zavagno@lam.fr LAM
If successful the PhD will take place at Aix-Marseille Université in Marseille, France.
Application’s deadline: April 9th 2018
Profil du candidat :
We are seeking for a PhD candidate interested to apply machine learning methods on large astrophysical datasets. The main challenge is to develop new tools to study star formation in the Galaxy. Such tools include classical ML methods, but also deep learning methods suited to the Big Data-Class databases used. Moreover, data come from different instruments, requiring to manage heterogeneous data in a multi dimensional space.
Formation et compétences requises :
Marster in
– Machine Learning
– Physics
– Data scientists
Adresse d’emploi :
If successful the PhD will take place at Aix-Marseille Université in Marseille, France.