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  Machine Learning methods for lattice field theory and urban studies

   Department of Mathematical Sciences

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  Dr Pavel Buividovich, Dr Carlos Medel-Vera  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

We are looking for strong candidates with a background in physics, mathematics or computer science to work on a collaborative PhD project led jointly by the Department of Mathematical Sciences and the School of Architecture at the University of Liverpool. The project focuses on applications of Machine Learning Methods to scientifically different but methodologically close problems in theoretical physics and urban studies.

With guidance and data provided by the experts at the School of Architecture, you will use Machine Learning methods such as deep CNNs and GANs to deal with real-world problems related to classification and generation of street art and urban landscapes. In collaboration with our industrial partner Art Recognition AG (Zurich), you will also have the opportunity to explore machine learning methods for artwork classification, focusing in particular on anomaly detection algorithms. Successful candidate will be very welcome to actively shape these open-ended projects.

You will also learn the framework of lattice QCD Monte-Carlo simulations, which is currently the only method for reliable calculations in the realm of strong nuclear interactions. For example, you may work on the extraction of transport coefficients from lattice QCD data, which involves a numerically ill-defined numerical analytic continuation procedure.

Throughout the project you will have access to comprehensive training in data science provided by the University of Liverpool with the Centre for Doctoral Training LIV.INNO, as well as to postgraduate training in theoretical physics at the Department of Mathematical Sciences.

This project will be carried out over 48 months starting in October 2024. A mandatory 6-months industry placement forms part of the project. This position will be funded jointly by STFC via Liv.Inno Center for Doctoral Training and by the School of the Arts.

Architecture, Building & Planning (3) Computer Science (8) Creative Arts & Design (9) Mathematics (25) Physics (29)

Funding Notes

The studentship will cover UK/EU/Worldwide tuition fees; a tax-free maintenance allowance will be paid. . Centre for Doctoral Training for Innovation in Data Intensive Science - University of Liverpool

Where will I study?

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