|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||4 years at the UKRI rate (currently £15,921 per annum for 22/23)|
|Placed On:||30th November 2021|
|Closes:||11th February 2022|
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 Mu3e experiment at PSI will look for extremely rare muon decays; in particular it is designed to try to identify the lepton flavour-violating decay of a muon to three electrons at the level of one event in 10^16. The experiment will use the latest advances in detector technology to identify electrons with high spatial and temporal resolution, and advanced pattern recognition algorithms will be implemented electronically to filter the data in real time.
In this project the student will apply the latest developments in machine learning to Mu3e event reconstruction and filtering, developing new techniques that could be faster, more flexible and/or more effective than conventional algorithms.
This could lead not only to the optimisation of the physics reach for the three-electron channel, but also the capability to perform real-time detailed analysis to look for different signatures. The student will start by developing and optimising algorithms in simulation, and then will have the opportunity to commission and test them in early data from the running experiment.
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.
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).
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.
Prof. Joel Goldstein (firstname.lastname@example.org), Prof. Henning Flaecher (email@example.com)
Subject Areas: Map your PhD to a maximum of 10 subject areas:
Particle Physics, Physics, Machine Learning, Data Science
Type / Role: