Machine learning for big astronomical data

While modern telescopes made large area of surveys of Galaxy and nearby galaxies, it is challenging task to extract and pin-point the exact locations of what we are looking for in these surveys. Recently, machine learning techniques has been developed well for pattern recognitions in images, that could potentially help to identify and classify astronomical objects.

The project is to develop and optimise machine learning to identify and classify supernova remnants, star-forming regions and young stellar objects on Herschel and NIKA survey of Galactic plane and nearby galaxies. The extracted data will be examined if supernova remnants compress the surrounding gas, and star-formation nearby.

This project will be funded by the STFC.
Applicants should apply to the Doctor of Philosophy in Physics and Astronomy with a start date of 1st October 2020.
https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/physics-and-astronomy

In the research proposal section of your application, please specify the project title and supervisors of this project. If you are applying for more than one project, please list the individual titles of the projects in the text box provided. In the funding section, please select ’I will be applying for a scholarship/grant’ and specify that you are applying for advertised funding from the STFC.

Applicants will need to submit the following documents with their application:
- post high school certificates and transcripts to date
- academic CV
- personal statement
- two academic references. Your references can either be uploaded with your application, or emailed by the referee to [Email Address Removed] or [Email Address Removed]