Machine learning and deep learning techniques for analyzing astrophysical observational data of compact objects with extreme physical properties - 2026_IDR_DEIB_45

Position: Research appointment (pre-doc) Institute: Polytechnic of Milano
Posted on: 04/06/2026 Deadline: 10/07/2026

Scientific-Disciplinary Group

09/IINF-05 - Information Processing Systems

Description

The research program involves the study of machine learning and deep learning methods applicable to the analysis of astrophysical observational data from compact objects with extreme physical properties, such as magnetars, X-ray polarization, (P)ULXs, super-Eddington accretion and winds; ultra-short X-ray transients, long-period radio transients, and compact binary systems consisting of two degenerate objects (such as two white dwarfs). The research involves developing algorithms to evaluate the output of simulations of astrophysical systems composed of compact objects and refining data analysis models based on neural networks, including multimodal ones

Number of positions

1

Funding body

Politecnico di Milano

Selection process

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In order to participate in the selection, please read the call ("bando") available at the following website: https://www.polimi.it/en/bandi-incarichidiricerca Oral test aimed at ascertaining candidates’ aptitude and suitability to carry out the research activity covered by the Fellowship, as well as at assessing their knowledge of English and/or other languages relevant to the research activities to be performed (up to 30 points) Relevance and pertinence of the publications, theses and scientific products attached to the research programme covered by the Fellowship (up to 30 points) Relevance and pertinence of previous research activities and work experience, if any, in relation to the research activity covered by the Fellowship (up to 20 points) Relevance and pertinence of their study programme to the research programme covered by the Fellowship (up to 20 points)