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Fast Inference for Online Data Selection at DUNE


   School of Physics

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  Dr Jim Brooke, 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.

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long baseline neutrino experiment, which will measure neutrino properties to exquisite precision. In particular, DUNE aims to resolve the neutrino mass hierarchy, as well as discovering CP violation in the lepton sector, which could explain the matter-antimatter asymmetry of the Universe. The Bristol DUNE group is active in data-acquisition and online selection for the DUNE far detector, as well as physics analyses at protoDUNE - a prototype detector operating at CERN. We have developed an object recognition network based on YOLO (You Only Look Once) for fast, online identification of neutrino interactions in the DUNE data. Selecting data of interest is critical, since the raw data rate will approach one zettabyte per year. In this project, we will study network acceleration and performance on a range of hardware platforms (including FPGA implementation using hls4ml), construct and operate a demonstrator system for use at protoDUNE, as well as study network performance against a range of neutrino phenomena. Opportunities for developing new ML applications within DUNE/protoDUNE physics analysis will also be available.

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:

Dr Jim Brooke ([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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