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.
Half the light ever emitted by stars in the Universe has been absorbed by cosmic dust. This increases to >90% emitted when looking at regions where star-formation is occurring. Luckily, the dust re-emits the energy absorbed in the far-infrared/sub-millimetre, and so by observing in these wavelengths we can understand these cold dark regions.
Our group in Cardiff is leading several international teams to obtain the best possible data in the submillimetre and millimetre to complement our existing maps from the Herschel Space Observatory. This includes large surveys ongoing on the James Clark Maxwell Telescope (JCMT) in Hawaii, IRAM in Spain, the Large Millimeter Telescope (LMT) in Mexico, and ALMA in Chile, to observe M31, M33 and other local galaxies (for example the DOWSING & Dustpedia surveys). In particular, the HASHTAG survey on the JCMT will produce the first ground-based sub-mm image of Andromeda increasing our resolution by over a factor of ~5. The student will have the opportunity to join these survey teams and help with the data collection.
The project will investigate the interplay between the cosmic dust and the other components of the interstellar medium (e.g., atomic gas, molecular gas, and metallicity). For example, we know dust provides a way to measure the ‘dark gas’ in galaxies and is a way to measure the physical conditions in the interstellar medium. However, very little quantitative analysis has been done extra-galactically due to previous limits on resolution. We also know relatively little about the dust itself, and our recent work discovered that the dust’s properties vary significantly across a galaxy. In this project the student will help develop new analysis tools to maximise the information from our observations (for example high-resolution SED-fitters, and hierarchical Bayesian fitting), and applying these techniques to our new high-resolution datasets.
We will then study the interstellar medium and star formation on the scale of individual giant molecular clouds. This includes investigating what is causing changes in the cosmic dust, the amount of dark gas, and what is heating the dust. We will then look at what regulates the star-formation process in galaxies. Whether it’s dominated by local properties (e.g., local density or radiation field), or larger-scale properties (e.g, galaxy morphology, disk dynamics). How global galaxy relations, like the correlation between surface-density of star-formation and gas (Schmidt-Kennicutt law), are built from the small scale giant molecular clouds will be investigated.
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]
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/