Fully Funded UKRI CDT AIMLAC PhD Scholarship: Application of Machine Learning for non-destructive beam profile measurements at CERN’s Large Hadron Collider (LHC) 

Funding provider: UK Research and Innovation (UKRI)

Subject areas: Physics

Project start date: 1 October 2023 (Enrolment open from mid-September)

Aligned programme of study: PhD in Physics

Mode of study: Full-time only

Project supervisor:

• Professor Niels Madsen

Project description:

Artificial Intelligence, Machine Learning and Advance 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.

Research theme: T1 - data from large science facilities

Beam instrumentation concerns the technologies needed to make direct measurements of beam observables such as beam position, intensity and size. These observations provide the diagnostic information to operate and improve the performance of accelerators and the associated transfer lines. For example, in order to maximize the collision rate at colliders - accelerator operators & physicists need measurements of the beam size throughout the acceleration cycle. Development of such an instrument for the LHC has proven to be particularly challenging, since the energy of the beam is too high to measure the profile by interacting directly with the beam itself. The aim of this project will be to develop a beam profile monitor for the LHC which is based on an idea first proposed in the 1960’s that allows to measure the beam profile (& other beam parameters) without interacting directly with the beam - but updated with cutting edge Timepix4 hybrid pixel detector technology that has been developed recently at CERN. A Beam Gas Ionisation (BGI) beam profile monitor is based on the detection of the residual particles that inevitably pervade the beampipe’s vacuum and which undergo ionisation as the beam flies through the pipe. The charged particles are directed towards the monitor by electromagnetic fields and - in the case of the LHC devices - will be directly detected by Timepix4 hybrid pixel detectors, its core element. The beam size is then inferred from the distribution of the detected electrons in real time, and the data organised to create a footage of the beam size evolution.

Specific goals of the project could include:

1. Optimisation of the electromagnetic field cage to transport ionisation electrons to the Timepix4 detector, including possible application of Machine Learning (ML) techniques to optimise the instrument performance;
2. Development of the Timepix4 based electron detector & associated data acquisition electronics, which must operate a few cm's from the LHC beam and acquire up to 160 GB/s of data;
3. Development of the real time data processing procedures to: i) extract the ionisation electron signal from various backgrounds (e.g. beam loss); ii) correct for known systematic effects and iii) publish the beam profiles of individual LHC bunches;
4. Study the potential to use Machine Learning (ML) techniques to i) optimise the accuracy of the measurement and ii) investigate possible applications of the BGI measurements for a real time beam orbit feedback system;
5. Study the potential to adapt the HL-LHC BGI design for transfer line applications, including medical accelerator facilities.

More information can be found at the UKRI CDT in Artificial Intelligence, Machine Learning & Advanced Computing (AIMLAC) website.

Please quote the project code (e.g. RS275 - AIMLAC8) for queries and within the application. If you wish to apply for more than one AIMLAC project, please complete a separate application for each project.

Applicants for PhD must normally hold an undergraduate degree at 2.1 level or a master’s degree with a minimum overall grade at ‘Merit’ (or Non-UK equivalent as defined by Swansea University). See ‘country specific information for European and international applicants (available at the bottom of the page)'. 

This scholarship covers the full cost of tuition fees and an annual stipend of £17,668.

Additional funds will be available for research expenses.

To apply, please complete your application online with the following information:

1. Course choice – please select Physics / Ph.D. / Full-time / 3 Year / October All applicants will need to select this course to formally link their application to this particular UKRI advertisement – please be reassured, though, even though is states 3 year, you will be applying for a 4 year PhD.

   If you are successful in receiving a scholarship, the course choice may change depending on the research area that you detail in your application.

2. Start year – please select 2023
3. Funding (page 8) –

• ‘Are you funding your studies yourself?’ – please select No
• ‘Name of Individual or organisation providing funds for study’ – please enter 'RS275 - AIMLAC8’

*It is the responsibility of the applicant to list the above information accurately when applying, please note that applications received without the above information listed will not be considered for the scholarship award.

One application is required per individual Swansea University led research scholarship award; applications cannot be considered listing multiple Swansea University led research scholarship awards

We encourage you to complete the following to support our commitment to providing an environment free of discrimination and celebrating diversity at Swansea University:

• Equality, Diversity and Inclusion (EDI) Monitoring Form (online form)

As part of your online application, you MUST upload the following documents (please do not send these via email):

• CV
• Degree certificates and transcripts (if you are currently studying for a degree, screenshots of your grades to date are sufficient)
• A cover letter including a ‘Supplementary Personal Statement’ to explain why the position particularly matches your skills and experience and how you choose to develop the project.
• Two references (academic or previous employer) on headed paper or using the Swansea University reference form. Please note that we are not able to accept references received citing private email accounts, e.g. Hotmail. Referees should cite their employment email address for verification of reference.
• Evidence of meeting English Language requirement (if applicable).
• Copy of UK resident visa (if applicable)

For enquiries, please contact Roz Toft (cdt-aimlac@swansea.ac.uk).

*External Partner Application Data Sharing – Please note that as part of the scholarship application selection process, application data sharing may occur with external partners outside of the University, when joint/co- funding of a scholarship project is applicable.