Advanced Machine Learning techniques applied to Astrophysical research

  • What it is

    Mobility experience with a research focus

  • Who it’s for

    Master students involved in the final research; PhD sandwich; Post Doc

Department

Department of Physics and Astronomy

Main research activities/topics/projects

The visitor will be part of the ECOGAL group at UniBo, where students and postdocs with a diverse background develop and apply machine learning techniques for the analysis and interpretation of large astrophysical datasets.

Some examples of the activities that can be carried out in this context include the application of conditional Invertible Neural Networks for the extraction of astrophysical parameters from large spectroscopic datasets in the optical/infrared and submillimetre, the development of techniques to reduce the model gap and the effect of data noise in these applications.

Working language

English

Special entry requirements

Advanced python skills mandatory, previous experience with Machine Learning techniques or astrophysical spectroscopy is desired.

Duration in months (min-max)

Master Research: 6-12

PhD sandwich: 6-12

Post Doc: 6-12

Contacts

Main scientific contact person

Prof. Leonardo Testi

+390512095763

Write an e-mail