PRESAGE: PREdicting Solar Activity using machine learning on heteroGEneous data

When:
18/06/2021 – 19/06/2021 all-day
2021-06-18T02:00:00+02:00
2021-06-19T02:00:00+02:00

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

Laboratoire/Entreprise : LIS / Université de Toulon
Durée : 34 months
Contact : adeline.paiement@univ-tln.fr
Date limite de publication : 2021-06-18

Contexte :
This postdoc position is part of the ANR-funded project PRESAGE: PREdicting Solar Activity using machine learning on heteroGEneous data. It concerns itself with the activity of the Sun, those events (e.g. flares, coronal mass ejections (CME)) are dynamical phenomena that may have strong impacts on the solar-terrestrial environment. Events of solar activity seem to be strongly associated with the evolution of solar structures (e.g. active regions, filaments), which are objects of the solar atmosphere that differ from the “quiet Sun” and which appear, evolve, and disappear over a period of days to months. The exact mechanisms of solar activity, and the links between solar structures and activity events, are still ill-understood.
Our project has three objectives related to solar physics, namely:
1) To improve our understanding of the mechanisms of solar activity
2) To enable the prediction of solar activity events such as flares, CMEs, radiation emission levels, and the fluxes of ionized particles likely to reach the Earth environment
3) To investigate the existence of typical temporal behaviours for 2D and 3D solar structures, i.e. filaments, active regions (AR), sunspots, and coronal holes
These objectives will be supported by two central objectives in machine learning (ML):
4) To develop new ML methods that can exploit the heterogeneous data generated by the many solar physics and space weather observation missions
5) To develop new ML algorithms that are guided by prior physics knowledge to increase their robustness and interpretability, and to reduce their need for large training datasets

Sujet :
The postdoctoral research will work with the PI and solar physics partners to develop physics-informed machine learning and deep learning methods for:
– 3D detection of solar structures
– Behavioural study of solar structures
– Prediction of solar activity events

Profil du candidat :
The candidate should:
– hold a PhD in machine learning, deep learning (DL), or computer vision
– have in-depth understanding of various DL models, and experience in creating new DL models
– have experience working with a wealth of data types including images and time series
– have experience with managing large datasets
An experience with multidisciplinary research (preferably with physics) would be desirable.

Formation et compétences requises :
– PhD in machine learning, deep learning (DL), or computer vision
– strong Python programming skills
– strong experimental skills, including the use of high performance computing clusters
– ability to work with and manage large datasets

Adresse d’emploi :
Laboratoire d’Informatique et Systèmes, équipe DYNamiques de l’Information (DYNI)

Université de Toulon, Campus de La Garde – La Valette, Avenue de l’Université, 83130 LA GARDE