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Identifying and characterising the highest redshift clusters and proto-clusters in huge multi-wavelength data sets


   School of Physics

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  Prof Malcolm Bremer, Prof Henning Flaecher  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

UKRI Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) CDT

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.

Galaxy clusters are highly sensitive probes of both the cosmological evolution of structure in the universe and the astrophysical effect of environment on galaxy evolution. Identifying and studying these systems during their early evolution has, up until now, been challenging to do in an unambiguous manner, but is vital to make progress on both the evolution of structure and galaxies. The field is due to be revolutionised by the availability of deep, large-volume data sets across multiple wavebands. Using this, we will be able to obtain a clearer view of these early clusters and proto-clusters, particularly whether our currently very limited picture is skewed by selection effects.

In order to identify the signatures of these systems from these multiple huge data sets, current selection techniques are unlikely to be of much use, potentially ending up with either too many false positives or too few genuine systems (or systems skewed in character by assumptions inherent in the selection technique), a significant issue given the volumes (both data volumes and physical volumes) involved. By applying appropriate ML and AI techniques to the discovery and characterisation of these systems we aim to efficiently generate statistically valid samples of these systems and compare them to our theoretical expectations and computer models of their evolution and growth in a way that is unlikely to be possible using current techniques.

Candidate requirements: 

Candidates should have completed an undergraduate degree (minimum 2(i) honours or equivalent) in a relevant subject, such as physics and astronomy, computer science, or mathematics.

Candidates should be interested in AI and big data challenges, and in the data from large science facilities research theme. You should have an aptitude and ability in computational thinking and methods including the ability to write software (or willingness to learn it).

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 http://www.bris.ac.uk/pg-howtoapply. Please select Physics (PhD) on the Programme Choice page. You will be prompted to enter details of the studentship in the Funding and Research Details sections of the form. Please make sure you include “AIMLAC CDT”, the title of studentship and the contact supervisor in your Personal Statement.

Contacts:

Prof. Malcolm Bremer ([Email Address Removed]), Prof. Henning Flaecher ([Email Address Removed])


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 £15,921 per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.
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