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The role of supermassive black holes in galaxy evolution through the eyes of Euclid

   School of Physics

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  Dr Sotiria Fotopoulou, Dr Andy Young, 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.

Supermassive black holes hold an undisputed role in galaxy evolution. The energetic feedback needed to regular the formation of new stars can only be provided by the energy available in the gravitational potential around a super massive black hole. However, the exact conditions under which this feedback operates remain elusive.

Next generation surveys such as the Euclid space telescope and the Vera Rubin Observatory will deliver unprecedented quality across the extragalactic sky. With resolution comparable to the Hubble space telescope and sensitivity on par to currently available pencil-bean surveys, Euclid will revolutionize our understanding of galaxy evolution.

A significant impediment in the exploitation of the Petabytes of the data produced, rendering prohibitive the modelling of data using traditional methods. The project will use cutting edge machine-learning accelerated scientific computation to identify actively accreting super massive black holes in the Euclid and LSST datasets and pin-point the role of black hole in galaxy evolution at the peak of cosmic activity, 3-5 billion years after the Big Bang.

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 and follow the instructions to apply online. This includes an online application for this project at 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.


Dr Sotiria Fotopoulou ([Email Address Removed]), Dr. Andy Young ([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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