Machine learning and deep learning techniques for analyzing astrophysical observational data of compact objects with extreme physical properties - 2026_IDR_DEIB_45
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
Job posting website
Number of positions
1
Funding body
Politecnico di Milano
Selection process
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