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.
Supersonic turbulent motions are thought to play a major role in star formation process. Theories have suggested that ’turbulence’ is responsible for shaping the interstellar medium (ISM), regulating star formation, and even setting the masses of stars as they form in young clusters. However, the properties of the turbulent motions in the ISM are difficult to determine: we can only use the doppler shifting of molecular emission lines to probe their velocities — giving us 1D, not 3D information — and these lines are often optically thick, such that their shapes are difficult to interpret.
Numerical simulations of the turbulent ISM can help here. By using simulations that track the formation and destruction of CO and other species, we can then make synthetic observations of the simulations and use these to study the properties of the turbulence. Since we know the ‘true’ underlying properties of the turbulence in our 3D simulations, we can then test how the standard techniques used on observational data perform on synthetic observations. We can also make use of a machine learning approach to develop new techniques for studying the properties of ISM turbulence in our simulations and observational datasets.
In this PhD, the student will perform magneto-hydrodynamics simulations of the turbulent ISM, using our world-leading ISM chemical/thermodynamical model (Clark et al. 2019). They will then perform radiative transfer postprocessing on the ISM simulations to create "mock" observations of commonly probed molecular lines (e.g. CO, HCO+, HCN, N2H+), which will then form the basis of an extensive machine learning study. The goal will be to see whether modern AI techniques can distinguish between different environmental conditions in the turbulent velocity fields. The student will then apply this technique to real molecular line data from nearby star-forming regions, to provide a new perspective on their turbulent evolution.
This PhD will introduce you to a wide range of ISM physics — such as ISM chemistry, molecular line radiative transfer, (magneto-)fluid dynamics — and also advanced statistics and machine learning techniques.
Start date: 1st October 2023
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 2023.
The partners include: JD Power UK, ATOS, DSTL, Mobileum, GCHQ, EDF, Amplyfi, DiRAC, Agxio, STFC, NVIDIA, Oracle, QinetiQ, 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 2023): 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 mid-February 2023. However, AIMLAC will continue to accept applications until the positions are filled.
For general enquiries, please contact Roz Toft [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/