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ML / DL for Astrophysics @ SKA and NEANIAS

Machine Learning and Deep Learning provide advanced algorithms and solutions for detecting structures in astronomical surveys.

The internship aims to develop cutting-edge techniques to perform automatic classification (supervised and unsupervised) of sources in multi-spectral astronomical maps in different computing infrastructures (HPC and Cloud).

IT technologies used: Python, Jupyter (, Tensorflow (, PyTorch (


Contacts: Simone Riggi (, Eva Sciacca (, Carmelo Pino (


DURATION: 2 – 4 months

More information

Burke, Colin J., et al. “Deblending and classifying astronomical sources with Mask R- CNN deep learning.” Monthly Notices of the Royal Astronomical Society 490.3 (2019): 3952-3965. Available from Arxiv: