The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.
Supernovae are catastrophic stellar explosions shaping the visible Universe and affecting many diverse areas of astrophysics. Supernovae arising from massive stars, referred to as core-collapse supernovae, play a major role in many intriguing astronomical problems since they produce neutron stars, black holes, and gamma-ray bursts. We are now living in the golden era of transient astronomy, with roughly 20000 transients discovered per year. The advent of the Legacy Survey of Space and Time (LSST) at the Vera Rubin Observatory will boost the number of yearly discoveries by a factor of 100, creating a new data challenge. Task-specific algorithms employed until now for transient’s classification have limitations in taming the zoo of transients.
The main project goal is to develop an Artificial Intelligence tool (deep learning algorithm) that can process time-series (e.g. luminosity evolution) and non-time-series (e.g. environment information) and that can identify core-collapse supernovae in two weeks from explosions, which is when we can retrieve crucial information about the progenitor nature. A secondary goal is to build such an AI tool in a way that is scalable enough to be applied to the environment of compact stars mergers producing gravitational waves. This application can predict the merger type (what objects are merging and their masses) and allow for rate and population studies at far distances.
Start date: 1st October 2022
The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing provides 4-year, fully funded PhD opportunities across broad research themes:
- T1: data from large science facilities (particle physics, astronomy, cosmology)
- T2: biological, health and clinical sciences (medical imaging, electronic health records, bioinformatics)
- T3: novel mathematical, physical, and computer science approaches (data, hardware, software, algorithms)
Its partner institutions are Swansea University (lead institution), Aberystwyth University, Bangor University, University of Bristol and Cardiff University.
Training in AI, high-performance computing (HPC) and high-performance data analytics (HPDA) plays an essential role, as does engagement with external partners, which include large international companies, locally based start-ups and SMEs, and government and Research Council partners. Training will be delivered via cohort activities across the partner institutions.
Positions are funded for 4 years, including 6-month placements with the external partners. The CDT will recruit 10 positions in 2022.
The partners include: We Predict, ATOS, DSTL, Mobileum, GCHQ, EDF, Amplyfi, DiRAC, Agxio, STFC, NVIDIA, Oracle, QinetiQ, Intel, IBM, Microsoft, Quantum Foundry, Dwr Cymru, TWI and many more.
More information, and a description of research projects, can be found at the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website. http://cdt-aimlac.org/cdt-research.html
How to apply:
To apply, and for further details please visit the CDT website http://cdt-aimlac.org/cdt-apply.html and follow the instructions to apply online.
This includes an online application for this project at (with a start date of 1st October 2022): https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/physics-and-astronomy)
Applicants should submit an application for postgraduate study via the Cardiff University webpages including:
• your academic CV
• a personal statement/covering letter
• two references, at least one of which should be academic
• Your degree certificates and transcripts to date.
In the "Research Proposal" section of your application, please specify the project title and supervisors of this project.
In the funding section, please select that you will not be self funding and write that the source of funding will be “AIMLAC CDT”
The deadline for applications for the UKRI CDT Scholarship in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) is 12th February 2022. However, AIMLAC will continue to accept applications until the positions are filled.
For general enquiries, please contact Rhian Melita Morris [Email Address Removed]
Eligibility:
The typical academic requirement is a minimum of a 2:1 physics and astronomy or a relevant discipline.
Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS) (https://www.cardiff.ac.uk/study/international/english-language-requirements)
Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it).
For more information on eligibility, please visit the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing website http://cdt-aimlac.org/