1st Artificial Intelligence Data Analysis (AIDA) School for Heliophysicists, Bologna

Soumis par mchane-y le lun, 14/10/2019 - 11:57

1st Artificial Intelligence Data Analysis (AIDA) School for Heliophysicists

 When: MONDAY, 20 JANUARY 2020 (ALL DAY) TO WEDNESDAY, 22 JANUARY 2020 (ALL DAY)

 Where:  CINECA - BOLOGNA OFFICES

 Registration: OPEN at https://eventi.cineca.it/en/node/1645/register

 Deadline for registration is NOVEMBER 29TH 2019.

 The number of participants for each edition is limited. Applicants will be selected according to their experience, qualifications and scientific interest BASED ON WHAT WRITTEN IN THE REGISTRATION FORM.

 Attendance is free.

 This course will be held in ENGLISH.

Coordinators: G.Lapenta, F. Delli Ponti, J.Amaya

Teachers: Morris Reidl (Jülich Supercomputer Center/ University of Iceland), Geert Jan Bex (Flanders Supercomputer Center), Peter Wintoft (Swedish Institute of Space Physics), AIDA consortium members.

Description:

 AIDA  (http://aida-space.eu/) is an European Commission Horizon 2020 project. Its goal is to encourage the use of Artificial Intelligence and Machine learning for the analysis of heliophysics data. We bring together the best european space scientists working in spacecraft observations, simulations, High Performance Computing and machine learning.

 The main objective of this school is to introduce the european heliophysics community to the domain of machine learning and data analysis.

Skills:

By the end of the course each student should learn:

 Basics of machine learning: supervised, unsupervised learning, neural networks

Space data gathering, handling and processing

What modern techniques are used in the domain of space physics

 What modern techniques are used in other applications outside physics

Target Audience:

The school is oriented towards established scientists, postdoctoral researchers, phd students and master students in space physics, with an interest in data analysis, who want to learn the basics of machine learning, and find inspiration to apply such techniques to their own research.

Pre-Requisites: Basic knowledge of python, jupyter notebooks, and space physics.