In the middle of applying to universities? | SHARE YOUR EXPERIENCE In the middle of applying to universities? | SHARE YOUR EXPERIENCE

A Deep (Learning) Dive into Solar Active Region Evolution and Flare Production (Advert ref: NUDATA23/EE/MPEE/BLOOMFIELD)

   Faculty of Engineering and Environment

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Dr Shaun Bloomfield  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

Solar flares, alongside coronal mass ejections (CMEs), are major contributors to space weather – changing conditions in the near-Earth space, magnetosphere and Earth’s upper atmosphere. Flares mostly occur in active regions (ARs); volumes of the solar atmosphere defined by the magnetic field. Plasma flows move magnetic field around and, after enough energy accumulates and conditions are suitable, ARs release stored energy as flares/CMEs. However, the conditions required to initiate flares/CMEs are unclear, limiting our ability to forecast them.

Recently, we built an infrastructure (FLARECAST) to explore many AR properties via machine-learning (ML) forecasting. However, we found that most information used by the ML methods is contained in a small number of AR properties. This is due to significant information redundancy since most AR properties were calculated from the same magnetic-field images. This is amplified by several AR properties aiming to quantify essentially the same (not directly observable) physical characteristics (e.g., proxies for free magnetic energy). Whole-image data has only recently begun being explored for forecasting via Deep Learning (DL), which should show improvement over previous ML methods that depend crucially on subjective choices of what AR properties to extract from magnetic-field images; DL explores all information in each magnetic-field image and its relation to supervised labels of flaring/non-flaring.

In this project you will use vectormagnetic field observations understand the evolution of AR magnetic fields and their relation to flare occurrence for a large statistical sample, encompassing flare-quiet ARs to those with flaring activity of high frequency and magnitude. You will seek to understand the physics leading to flares with the ultimate aim of improving our capacity to forecast them. You will have access to state-of-the-art ML/DL methods and will apply these in large-scale processing of 10s-100s TB of data. This PhD concerns the magnetic conditions that power/initiate flares/CMEs, with the potential to impact on the quality/timeliness of forecasting adverse space-weather conditions and increasing the operational capacity of space-weather forecast centres (e.g., Met Office). As the project develops, you will have the opportunity to collaborate with national/international colleagues in the space-weather forecasting community.

This project suits students with an Astrophysics, Physics or Applied Mathematics degree. Prior experience with programming is desirable (e.g., Python/IDL), but training in all necessary skills will be provided. The student will be supported to publish their work in leading peer-reviewed journals and will have opportunities to present at national/international conferences. To informally discuss this opportunity please contact [Email Address Removed].

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 application form, see

Please note:

You must include the relevant advert reference/studentship code (e.g. NUDATA23/…) 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. For informal enquiries, email Professor James McLaughlin ([Email Address Removed]). 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

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).


Georgoulis, M.K., Bloomfield, D.S. + 26 co-authors (2021) Journal of Space Weather and Space Climate, 11, 39
The flare likelihood and region eruption forecasting (FLARECAST) project: flare forecasting in the big data & machine learning era
Campi, C., Benvenuto, F., Massone, A.M., Bloomfield, D.S., Georgoulis, M.K., Piana, M. (2019) The Astrophysical Journal, 883, 150
Feature Ranking of Active Region Source Properties in Solar Flare Forecasting and the Uncompromised Stochasticity of Flare Occurrence
Kontogiannis, I., Georgoulis, M.K., Guerra, J.A., Park, S.-H., Bloomfield, D.S. (2019) Solar Physics, 294, 130
Which Photospheric Characteristics Are Most Relevant to Active-Region Coronal Mass Ejections?
Leka, K.D., Park, S.-H., Kusano, K., Andries, J., Barnes, G., Bingham, S., Bloomfield, D.S. + 17 co-authors (2019) The Astrophysical Journal, 881, 101
A Comparison of Flare Forecasting Methods. III. Systematic Behaviors of Operational Solar Flare Forecasting Systems
Leka, K.D., Park, S.-H., Kusano, K., Andries, J., Barnes, G., Bingham, S., Bloomfield, D.S. + 17 co-authors (2019) The Astrophysical Journal Supplement Series, 243, 36
A Comparison of Flare Forecasting Methods. II. Benchmarks, Metrics, and Performance Results for Operational Solar Flare Forecasting Systems
Domijan, K., Bloomfield, D.S., Pitié, F. (2019) Solar Physics, 294, 6
Solar Flare Forecasting from Magnetic Feature Properties Generated by the Solar Monitor Active Region Tracker
Park, S.-H., Guerra, J.A., Gallagher, P.T., Georgoulis, M.K., Bloomfield, D.S. (2018) Solar Physics, 293, 114
Photospheric Shear Flows in Solar Active Regions and Their Relation to Flare Occurrence
McCloskey, A.E., Gallagher, P.T., Bloomfield, D.S. (2018) Journal of Space Weather and Space Climate, 8, A34
Flare forecasting using the evolution of McIntosh sunspot classifications
Florios, K., Kontogiannis, I., Park, S.-H., Guerra, J.A., Benvenuto, F., Bloomfield, D.S., Georgoulis, M.K. (2016) Solar Physics, 293, 28
Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
Barnes, G., Leka, K.D., Schrijver, C.J., Colak, T., Qahwaji, R., Ashamari, O.W., Yuan, Y., Zhang, J., McAteer, R.T.J., Bloomfield, D.S. + 8 co-authors (2016) The Astrophysical Journal, 829, 89
A Comparison of Flare Forecasting Methods. I. Results from the “All-Clear” Workshop
McCloskey, A.E., Gallagher, P.T., Bloomfield, D.S. (2016) Solar Physics, 291, 1711
Flaring Rates and the Evolution of Sunspot Group McIntosh Classifications
Ahmed, O.W., Qahwaji, R., Colak, T., Higgins, P.A., Gallagher, P.T., Bloomfield, D.S. (2016) Solar Physics, 283, 157
Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection
PhD saved successfully
View saved PhDs