Optimizing AI to Forecast Dangerous Space Weather (Ref: NUDATA24/EE/MPEE/SMITH)

   Faculty of Engineering and Environment

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  Dr Andy Smith  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Overview of the CDT

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 https://research.northumbria.ac.uk/nudata/ for full information.

About the Project

Space weather describes the variable conditions in near-Earth space, driven by the interaction between the continuous outflow of the Solar atmosphere (the solar wind) and the Earth. Space weather is often benign, representing a steady state to which our infrastructure is well designed. However, during global intense events known as geomagnetic storms the near-Earth space environment becomes energized: these hazardous conditions can damage or destroy key infrastructure such as satellites and power networks.

Space weather forecasting is a relatively young field, yet one that has advanced significantly in the last decade, particularly with the adoption of AI methods. Detailed evaluation of the “first wave” of models has highlighted where we urgently need to improve our capabilities. This project will target one (or more) key avenues of enquiry, to enable the next-generation of space weather forecasting models that we require, for example:

A) Damaging space weather events are rare, with major (but localised) damage occurring a few times a decade. However, this raises a key issue for our ability to train advanced AI models. Whilst we have several decades of data available, from ground observatories and satellites, the events we need to forecast appear infrequently. This manifests as a severe data imbalance. How do we then best produce accurate forecasts, minimizing false alarms yet providing the warning we require?

B) Near-Earth space reacts to the solar wind on a huge range of timescales. Even fast changes in the character of the solar wind can initiate both immediate (~minutes) and delayed (~hours) consequences. Forecasting models need to be provided with an input that summarizes recent conditions, but what volume of historical input do we need to provide? If the time interval is extensive, requiring a very large input vector, can we reduce the dimensionality of this input while retaining the key information? There are numerous parameters that we can use to describe the solar wind, but which ones provide the most benefit to a multivariate forecasting models?

You will have access to state-of-the-art methods and the opportunity to work collaboratively with a large and friendly team, including working with experts and scientists from around the globe. You will have the opportunity to travel, presenting your work at conferences both in the UK and internationally.

We welcome applicants with a background in physics, applied mathematics, computer science or other related disciplines. Prior experience in scientific computing or plasma physics is a benefit, but we will support you to gain all the skills you need to do your project. You will also have the opportunity to meet and discuss your work with space weather stakeholders, such as the UK Met Office, in order to explore potential benefits of your project beyond academia.

Academic Enquiries

This project is supervised by Dr Andy Smith. For informal queries, contact [Email Address Removed]. For all other enquiries relating to eligibility or application process contact Admissions ([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 15th January 2024. 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 i.e. 2:1 (or equivalent GPA from non-UK universities with preference for 1st-class-honours); or a Masters (preference-for-Merit-or-above);
  • Appropriate IELTS score, if required.

To be classed as a Home student, candidates must:

  • 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.

 For further details on how to apply see


You must include the relevant advert reference 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 all the projects you are interested in (e.g. 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).

Deadline for applications: 31-Jan-2024

Start date of course:  23-Sept-2024

Northumbria University is committed to creating an inclusive culture where we take pride in, and value, the diversity of our postgraduate research students. We encourage and welcome applications from all members of the community. The University holds a bronze Athena Swan award in recognition of our commitment to advancing gender equality, we are a Disability Confident Leader, a member of the Race Equality Charter and are participating in the Stonewall Diversity Champion Programme. We also hold the HR Excellence in Research award for implementing the concordat supporting the career Development of Researchers and are members of the Euraxess initiative to deliver information and support to professional researchers.

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

Funding Notes

The 4-year studentship is available to Home and international (including EU) students and includes a full stipend at UKRI rates (for 2023/24 full-time study this was £18,622 per year) and full tuition fees. Studentships are also available for applicants who wish to study on a part-time basis in combination with work or personal responsibilities.


Smith, A. W., Forsyth, C., Rae, I. J., et al. (2022). On the considerations of using near real time data for space weather hazard forecasting. Space Weather, 20, e2022SW003098. https://doi.org/10.1029/2022SW003098
Smith, A. W., Forsyth, C., Rae, I. J., et al. (2021). Forecasting the Probability of Large Rates of Change of the Geomagnetic Field in the UK: Timescales, Horizons and Thresholds. Space Weather, 19, e2021SW002788. https://doi.org/10.1029/2021SW002788
Smith, A. W., Rae, I. J., Forsyth, C., et al. (2020). Probabilistic Forecasts of Storm Sudden Commencements from Interplanetary Shocks using Machine Learning. Space Weather, 18, e2020SW002603. https://doi.org/10.1029/2020SW002603

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