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.
Much like its terrestrial counterpart, space weather can damage or destroy critical infrastructure that we rely on every day, such as satellites and power networks. It is critically important to understand and be able to forecast extreme space weather. During events known as geomagnetic storms, huge amounts of energy are transferred into near-Earth space, and correspondingly extreme electrical currents flow into the Earth’s ionosphere known as Birkeland currents. Meanwhile the magnetic field of the Earth changes rapidly on the ground during geomagnetic storms. These rapid changes in the magnetic field cause geomagnetically induced currents (GICs) in extended, grounded infrastructure such as pipelines and power networks. These GICs can damage or destroy critical infrastructure.
State-of-the-art research has discovered when and where extreme Birkeland currents are most likely to flow towards the Earth, but we still do not fully understand how these are linked to GICs. Because the two systems are closely interlinked, understanding this link will yield a key avenue to relate the drivers of space weather to impacts on the ground.
We have decades of observations from constellations of orbiting spacecraft and global networks of magnetic field observatories monitor the near-Earth environment. These datasets provide measurements of Birkeland currents flowing in and out of Earth’s atmosphere, as well as magnetic signatures on the ground which lead to GICs.
You will combine these two and apply machine learning methods in order to assess how the two are linked, and how extremes in one relate to the other. Are extreme ionospheric currents sufficient or necessary to observe extreme magnetic signatures? Are they seen in the same region, and do they share the same drivers?
This problem has both temporal and spatial aspects, and this may guide the choice of machine learning techniques during the project. You will explore different approaches to see which will lead to the next significant advance in this field. Some potentially productive avenues include using transformer-based models, recurrent neural networks, normalizing flows, or extreme value analysis to examine the link between the two phenomena.
You will have regular one-on-one meetings with supervisors and will take part in activities within our larger research group, including presenting work to colleagues and attending group seminars to learn about the rest of the group’s work. You will develop state-of-the-art methods and the opportunity to work collaboratively with a large team, working with other scientists both in the UK and around the world. You will have the opportunity to travel, presenting your work at conferences nationally and internationally. You will also publish your work in leading journals.
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 desirable, but we will support you to gain all the skills you need to execute the project. The preferred language for the project is Python, but knowledge of Python is not necessary.
This project is supervised by Dr Andy Smith and Dr John Coxon. For informal queries, contact [Email Address Removed] and [Email Address Removed]. For all other enquiries relating to eligibility or application process contact Admissions ([Email Address Removed]).
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.
- 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/studentship code (e.g. NUDATA24/…) in your application.
Deadline for applications : 31st January 2024
Start date of course : 23rd September 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.