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Understanding Solar Active Region Evolution using Machine Learning


School of Science and Engineering

About the Project

Active regions (ARs) are intense and complex regions of magnetic activity on the Sun that can produce huge, energetic eruptions such as solar flares and coronal mass ejections (CMEs). These eruptions can have an impact on Earth and in particular on our technology in the form of “Space Weather”. For example, solar energetic particles can cause electrical failure in satellites and geomagnetic storms triggered by CMEs can result in GPS errors. Understanding how active regions evolve and whether they will produce eruptions is an important topic in Solar Physics and Space Weather prediction.

This project will use Machine Learning techniques to detect and track solar ARs, with the aim of addressing the following research questions:
• How do ARs evolve spatially and temporally?
• Hence, can we predict how an observed AR will evolve as it crosses the solar disc? This result would have important implications for Space Weather prediction, as eruptions occurring near disc centre are more likely to be Earth-directed.
• Can we further predict how an AR will evolve as it rotates out of our field of view (around the back side of the Sun)? This ability would enable more accurate modelling of the Sun’s atmosphere on a global scale and hence more accurate Space Weather modelling, taking this as a lower boundary condition for heliospheric simulations.
• The project will involve
• The development of Machine Learning models for the detection and tracking of solar ARs.
• Analysis and assimilation of solar observations into models.
• Data-driven simulations of solar magnetic fields, taking detected observed ARs as input.

For informal enquiries about the project, contact Dr Karen Meyer ()
For general enquiries about the University of Dundee, contact


QUALIFICATIONS
Applicants must have obtained, or expect to obtain, a first or 2.1 UK honours degree, or equivalent for degrees obtained outside the UK in a relevant discipline.

English language requirement: IELTS (Academic) score must be at least 6.5 (with not less than 5.5 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s English language requirements are available online: http://www.dundee.ac.uk/guides/english-language-requirements.


APPLICATION PROCESS

Step 1: Email Dr Karen Meyer () to (1) send a copy of your CV and (2) discuss your potential application and any practicalities (e.g. suitable start date).

Step 2: After discussion with Dr Meyer, formal applications can be made via UCAS Postgraduate. When applying, please follow the instructions below:

Apply for the Doctor of Philosophy (PhD) degree in Mathematics: https://digital.ucas.com/coursedisplay/courses/c1f40893-a92e-d5d9-e320-c079d444d740. Select the start date and study mode (full-time/part-time) agreed with the lead supervisor.

In the ‘provider questions’ section of the application form:
- Write the project title and ‘FindAPhD.com’ in the ‘if your application is in response to an advertisement’ box;
- Write the lead supervisor’s name and give brief details of your previous contact with them in the ‘previous contact with the University of Dundee’ box.

In the ‘personal statement’ section of the application form, outline your suitability for the project selected.

Funding Notes

There is no funding attached to this project. The successful applicant will be expected to provide the funding for tuition fees and living expenses, via external sponsorship or self-funding.

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