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).
Contacts: Simone Riggi (firstname.lastname@example.org), Eva Sciacca (email@example.com), Carmelo Pino (firstname.lastname@example.org)
DURATION: 2 – 4 months
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: https://arxiv.org/abs/1908.02748