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 (https://jupyter.org/), Tensorflow (https://www.tensorflow.org/), PyTorch (https://pytorch.org/)
Contacts: Simone Riggi (simone.riggi@inaf.it), Eva Sciacca (eva.sciacca@inaf.it), Carmelo Pino (carmelo.pino@inaf.it)
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: https://arxiv.org/abs/1908.02748