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[AIMLAC CDT Studentship] Simulations of massive galaxies and their circumgalactic medium


   Cardiff School of Physics and Astronomy


About the Project

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. 

Galaxies live in large, dark matter-dominated, quasi-spherical structures called haloes. The gaseous component of these haloes is also known as the circumgalactic medium (CGM) and comprises the reservoir of material from which the embedded galaxies can grow. Galaxies do not only passively accrete gas from their environment, they also drive large-scale outflows into the CGM. These outflows enrich the CGM, magnetize it, and drive shocks and turbulence. Therefore, if we wish to understand the evolution of galaxies, we need to study the CGM. This PhD project will focus on the environments of galaxies more massive than the Milky Way. Such massive haloes are expected to host volume-filling hot gas with temperatures close to the virial temperature, which increases with halo mass. Radiative cooling allows some fraction of the gas, especially dense or metal-rich gas, to cool down to much lower temperatures. This creates a multiphase medium where the hot, diffuse gas and the cooler, denser gas are in approximate pressure equilibrium. The massive galaxies themselves behave very differently to lower mass galaxies, because they no longer rapidly grow and have mostly switched off star formation, a process known as quenching. Observations of the CGM have shown a large reservoir of cool gas even around galaxies with no cold interstellar medium or ongoing star formation. This came as a surprise: why does this cool gas not accrete onto the central galaxy and fuel star formation? These observations have thus led to the suggestion that our theoretical understanding is incomplete. This project will use cosmological, magnetohydrodynamical simulations, which start when the universe was very young and follow not just the formation of galaxies but also the large-scale structure of the universe. Traditionally, such simulations focus most of their computational effort on creating the best galaxies and incorrectly assume that the treatment of the CGM is sufficiently accurate. New numerical methods are being developed in order to increase the resolution of the gas around galaxies and study the massive galaxy haloes in more detail than has ever been possible before. The PhD student will take charge of developing and running galaxy simulations with enhanced CGM resolution and investigate the importance of various physical processes, such as magnetic fields and thermal conduction. A single simulation is expected to generate 15 TB of data and will thus require novel analysis techniques. After developing a successful analysis package, the student will have the opportunity to exploit the simulation data to study gas accretion, large-scale outflows, chemical enrichment, and other properties of the CGM. By comparing their simulations to existing observations and making predictions for future observations, the PhD student will be able to reveal the crucial role the CGM plays in the formation of massive galaxies and to put galaxy formation models to the test.

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, please visit the CDT website http://cdt-aimlac.org/cdt-apply.htmland 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

• two references, at least one academic.

• Your degree certificates and transcripts. 

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   

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/ 


Funding Notes

The UK Research and Innovation (UKRI) fully funded scholarships cover the full cost of 4 years tuition fees, a UKRI standard stipend of currently £17,668per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.

References

Cosmological simulations of the circumgalactic medium with 1 kpc resolution: enhanced HI column densities
Freeke van de Voort, Volker Springel, Nir Mandelker, Frank C. van den Bosch, Rüdiger Pakmor
Monthly Notices of the Royal Astronomical Society: Letters, Volume 482, Issue 1, p.L85-L89

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