Using machine learning to unravel the complex dynamics of space plasma turbulence (Advert ref: NUDATA23/EE/MPEE/STAWARZ)

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

PhD project description

Turbulence is a fundamental physical process in both neutral fluids, such as the ocean and atmosphere, and plasmas, such as the solar wind, planetary magnetospheres, interstellar medium, and black hole accretion discs. In the context of space and astrophysical plasmas, turbulence plays a key role in the acceleration and heating of particles, structure formation, particle scattering, and energy transport. However, despite its important role in these systems, the highly nonlinear, multi-scale nature of the phenomenon makes it one of the most enigmatic problems in classical physics.

One of the major recent advances in the study of space plasma turbulence, which has been enabled by the launch of cutting-edge spacecraft missions, has been the detailed examination of the relationship between turbulence and another fundamental phenomenon that occurs within plasma systems called magnetic reconnection. Magnetic reconnection releases energy stored in twisted and sheared magnetic fields by enabling a sudden change in the magnetic topology. The released energy is then transferred into the energy of the charged particles in the plasma. With four closely spaced satellites and measurements up to 100x faster than other missions, NASA’s Magnetospheric Multiscale (MMS) mission provides exactly the type of measurements that are needed to observe and examine the interplay between turbulence and magnetic reconnection to a degree that has not been possible before. 

In this project, you will work with researchers in the Solar & Space Physics group at Northumbria University and the wider international MMS science team to explore how to use machine learning techniques on the observational datasets provided by MMS, and potentially other space plasma missions, to identify and classify magnetic reconnection events and characterise their physical impact within turbulent plasmas. Previous work by the project supervisor has used this dataset to systematically identify and examine magnetic reconnection within turbulent regions by hand; however, the development of machine learning methodologies offers an exciting new opportunity that can drastically expand our database of turbulent magnetic reconnection events and allow us to glean new insight into its interplay with the turbulence.

About the Supervisor

Julia Stawarz is a Royal Society University Research Fellow, a member of the science team for the Magnetospheric Multiscale Mission, and works closely with the Parker Solar Probe and Solar Orbiter spacecraft. Her research focuses on the fundamental physics of turbulence, magnetic reconnection and other small scale plasma phenomena, as well as their impact on Earth’s magnetosphere and the solar wind.

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 https://www.gov.uk/healthcare-immigration-application
• If you need to apply for a Student Visa to enter the UK, please refer to the information on https://www.gov.uk/student-visa. 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 https://www.gov.uk/guidance/travel-to-england-from-another-country-during-coronavirus-covid-19
• 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

https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

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).

Deadline for applications: 31st January 2023

Start Date: 25th September 2023