Reconstructing and analysing space plasma data using machine learning (Ref: NUDATA24/EE/MPEE/GOETZ)

   Faculty of Engineering and Environment

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  Dr Charlotte Goetz, 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 for full information.

Project Description

Comets are small bodies that journey through the solar system on often highly elliptical orbits. As they near the Sun, the ices on the surface sublimate and gas and dust escape into space. This is sometimes visible by naked eye as stunning cometary tails, but it also has consequences for the solar wind, the plasma permeating the solar system. As the cometary gases encounter the solar wind and its magnetic field, the two plasmas interact and form an intricate cometosphere. The European Space Agency’s Rosetta mission explored this region for over two years with the goal to understand the processes in this unique plasma.

This project will focus on the electron environment of comet 67P/Churyumov-Gerasimenko. The Rosetta mission carried an electron spectrometer to measure the energy distribution function of the electrons in the plasma. Its main objective is to contribute to the characterisation of the plasma and emissions near the comet. However, due to instrumental and operational constraints, the field of view and data quality are limited. Data cleaning has been performed by hand, however this is inefficient and does not allow for large scale data exploitation. The dataset needs to be reconstructed in order to characterise the electron environment on long timescales and also efficiently perform smaller studies, e.g. into the influence of the infant bow shock, a newly discovered boundary in the plasma environment, on the electron pitch angle and energy distributions.

Project objectives are:

  1. Cleaning and reconstructing the data provided by the electron sensor. This will use machine learning techniques, such as autoencoders to denoise and reconstruct the data when the measurement quality is degraded. The candidate will familiarize themselves with machine learning techniques, as well as space instrument design and operations. The reconstructed dataset will be invaluable to the community for years to come.
  2. Characterise the electron environment of the comet on timescales of years. Electrons at comet 67P have been shown to influence the generation of UV aurorae and to contribute to the formation of critical boundaries. Understanding their distribution over large timescales is part of an effort to characterise the plasma environment of the comet with the available measurements.
  3. Investigate the influence of boundaries on the electron distribution function. At least two plasma boundaries at the comet have been shown to depend on the electron population. A) The infant bow shock has been shown to be related to heating of electrons, however, this heating is not observed at all crossings of the boundary. A more detailed investigation of these crossings could offer an explanation for such a discrepancy. B) The diamagnetic cavity at a comet, a region free of any magnetic field, has been related to a change in electron collisions, but how this change affects the electron population inside the diamagnetic cavity is an open and important question.

This project would be suitable for a student with a background in physics, applied mathematics or closely-related physical science. Prior knowledge of space plasma physics is not necessary. Prior knowledge of a programming language or machine learning is desirable but training in all necessary skills will be provided. You will have the opportunity to travel, presenting your work at conferences both in the UK and internationally.

Academic Enquiries

This project is supervised by Dr. Charlotte Goetz. 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  

In your application, please include the advert reference.

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) 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.


Goetz et al. (2022) doi:10.1007/s11214-022-00931-1
Goetz et al. (2021) doi:10.5194/angeo-39-379-2021
Madanian et al. (2020) doi:10.1016/j.pss.2020.104924

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