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  Using Machine Learning techniques to extract solar oscillations from Solar Orbiter data (Advert ref: NUDATA23/MPEE/MCLAUGHLIN)

   Faculty of Engineering and Environment

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  Prof James McLaughlin  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

About the Centre for Doctoral Training

This project is being offered as part of the STFC Centre for Doctoral Training in Data Intensive Science, called NUdata, which is a collaboration between Northumbria and Newcastle Universities, STFC, and a portfolio of over 40 industrial partners, including SMEs, large/multinational companies, Government and not-for profit organisations, and international humanitarian organisations. Please visit for full information.

PhD project description

The Sun is our nearest star and is the powerhouse of our Solar System. Investigating fundamental processes, such as solar eruptions and the birth of the solar wind, are important to further our understanding of the Sun (and, through this, all stars). There is currently strong scientific interest in understanding the energetic coupling of the layers of the solar atmosphere, and international interest in one of the primary candidates - solar oscillations - is at an all-time high. Several current and upcoming missions are designed to further our understanding of solar oscillations, and one of these missions is Solar Orbiter which is in an eccentric orbit of the Sun and is currently taking data. It is expected that these data will give a deeper insight into solar oscillations as well as the important role these wave motions play throughout all layers of the Sun’s atmosphere. Understanding the genesis and behaviour of these waves is crucial since they are believed to play a central role in energetic coupling between the different solar layers and can be exploited as a diagnostic tool (via magneto-seismology) for measuring the local magnetic field.

You will apply machine learning techniques to extracting information from Solar Orbiter data. You will learn various, relevant machine learning techniques via the taught part of the CDT and, at the same time, will become an expert in data analysis of a flagship European Space Agency spacecraft (Solar Orbiter). This will put you in a strong position to pursue both solar data analysis research and/or data-science-related careers after your PhD.

You will join a strong, supportive Solar and Space Physics research group ( ) which pursues high-international-priority research in Solar Physics. The Group plays multiple key roles in solar instruments and missions (including Solar Orbiter), has regular discussion sessions (including with visitors) and runs an established research seminar series and journal club; all of which creates an ideal research environment to support your learning. This PhD project would be suitable for applicants with undergraduate and/or masters degrees in Physics, Astrophysics, Mathematics, Statistics or Computer Science (or related disciplines). Prior experience with astrophysical/solar data and/or machine learning would be a benefit, but this PhD will provide you with specific training in all relevant areas. You will conduct research investigations, write publications, disseminate your findings at national and international conferences, and engage in international collaborations.

Recruitment Event

You will join a strong and supportive research team. To help better understand the aims of the CDT and to meet the PhD supervisors, we are hosting a day-long event on campus on Monday 9th January 2023.

At that event, there will be an opportunity to discuss your research ideas, meet potential PhD supervisors, as well as hear from speakers from a variety of backgrounds (academia, industry, government, charity) discussing both STFC and data science as well as their personal paths and backgrounds. Click here for details.

Eligibility Requirements:

  • Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
  • Appropriate IELTS score, if required.
  • Applicants cannot apply for this funding if they are already a PhD holder or if currently engaged in Doctoral study at Northumbria or elsewhere.

Please note: to be classed as a Home student, candidates must meet the following criteria:

  • Be a UK National (meeting residency requirements), or
  • have settled status, or
  • have pre-settled status (meeting residency requirements), or
  • have indefinite leave to remain or enter.

If a candidate does not meet the criteria above, they would be classed as an International student. Applicants will need to be in the UK and fully enrolled before stipend payments can commence, and be aware of the following additional costs that may be incurred, as these are not covered by the studentship.

  • Immigration Health Surcharge
  • If you need to apply for a Student Visa to enter the UK, please refer to the information on It is important that you read this information very carefully as it is your responsibility to ensure that you hold the correct funds required for your visa application otherwise your visa may be refused.
  • Check what COVID-19 tests you need to take and the quarantine rules for travel to England
  • Costs associated with English Language requirements which may be required for students not having completed a first degree in English, will not be borne by the university. Please see individual adverts for further details of the English Language requirements for the university you are applying to.

How to Apply

For further details of how to apply, entry requirements and the application form, see

Please note:

You must include the relevant advert reference/studentship code (e.g. STFC23/…) in your application.

If you are interested in more than one of the Northumbria-hosted NUdata research projects, then you can say this in the cover letter of your application and you can rank up to three projects you are interested in (i.e. first choice, second choice, third choice). You are strongly encouraged to do this, since some projects are more popular than others. You only need to submit one application even if you are interested in multiple projects (we recommend you submit your application to your first choice).

We offer all applicants full guidance on the application process and on details of the CDT. Please contact the Principal Supervisor of the project(s) [Email Address Removed] for project-specific enquiries.

Deadline for applications: 31st January 2023

Start Date: 25th September 2023

Computer Science (8) Geology (18) Mathematics (25) Physics (29)

Funding Notes

The studentship supports a full stipend, paid for four years at UKRI rates (for 2022/23 full-time study this is £17,668 per year), full tuition fees and a Research Training and Support Grant (for conferences, travel, etc).


Anfinogentov, [McLaughlin] et al. (2022) “Novel data analysis techniques in coronal seismology”, Space Science Reviews, 218, 9,
Banerjee, [McLaughlin] et al. (2021) “Magnetohydrodynamic Waves in Open Coronal Structures”, Space Science Reviews, 217, 76,
Zimovets, McLaughlin, et al. (2021) “Quasi-Periodic Pulsations in Solar and Stellar Flares: A Review of Underpinning Physical Mechanisms and Their Predicted Observational Signatures”, Space Science Reviews, 217, 66,
Weberg, Morton & McLaughlin (2020) “Using Transverse Waves to Probe the Plasma Conditions at the Base of the Solar Wind”, Astrophysical Journal, 894, 1,
Morton, Weberg & McLaughlin (2019) “A basal contribution from p-modes to the Alfvénic wave flux in the Sun’s corona”, Nature Astronomy, 3, 223,

Where will I study?