The Machine Learning and Data Applications Group in Solar Physics in the School of Mathematics and the Monash Data Futures Institute at Monash University and the Centre of Astrophysics at University of South Queensland ran a workshop on Machine Learning Algorithms for Solar, Stellar and Space Physics.
The workshop aimed to foster Solar and Stellar physics and Space Physics research through the application of ML (Machine Learning) and Deep Learning techniques. The workshop schedule was split between invited talks from researchers working with ML, and two training sessions to up-skill researchers on the use of AI and ML techniques.
The two training sessions run by ADACS focused on:
Workshop information and materials can be found on ADACS' GitHub account.
Check out some of our other training projects.
Most ECRs do not receive any formal training in critical software engineering skills. The ASA (Astronomical Society of Australia) collaborated with ADACS to run an intensive workshop to address this knowledge gap.
A four-part ML (machine learning) workshop consisting of a lecture on the history of ML and it's applications in astronomy research and three deep-dive sessions to apply ML techniques to astronomy data sets.
The NextFlow training provided comprehensive instruction on workflow orchestration, including running and developing NextFlow workflows, using containers (docker and singularity), configuring NextFlow with SLURM, and covering best practices.