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Machine Learning for CMS Trigger

   School of Physics

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  Dr Sudarshan Paramesvaran, 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.

A major upgrade of the Large Hadron Collider from 2024 will increase the rate of proton collisions to an unprecedented 10 billion per second. A sophisticated data processing system - the “L1 Trigger” - will process each event to identify interesting collisions for storage and further analysis; events not selected are lost forever. The L1 Trigger is constructed of custom hardware processors using high-speed optical links and FPGA devices. It can receive data at 60 Tb/s, and must process each event in less than 10𝜇s. In this project we will develop fast machine learning algorithms to identify interesting collisions, such as production of a pair of Higgs bosons, for the CMS L1 Trigger. The physics performance of these networks will be studied and optimised using Monte-Carlo simulation. The trade-off between physics performance and inference latency and resource usage will be studied for a range of networks types. Ultimately, candidate algorithms will be implemented in FPGAs and demonstrated in real processor boards.

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 Sudarshan Paramesvaran ([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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