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Funded Ph.D opportunities

Please see below

Stellar atmospheres and their magnetic helicity fluxes (PhD)

Supervisors: Simon Candelaresi, Radostin Simitev, David MacTaggart, Robert Teed
Relevant research groups: Continuum Mechanics - Fluid Dynamics and Magnetohydrodynamics

Our Sun and many other stars have a strong large-scale magnetic field with a characteristic time variation. We know that this field is being generated via a dynamo mechanism driven by the turbulent convective motions inside the stars. The magnetic helicity, a quantifier of the field’s topology, is and essential ingredient in this process. In turbulent environments it is responsible for the inverse cascade that leads to the large-scale field, while the build up of its small-scale component can quench the dynamo.
In this project, the student will study the effects of magnetic helicity fluxes that happen below the stellar surface (photosphere), within the stellar atmosphere (chromosphere and corona) and between these two layers. This will be done using two-dimensional mean field simulations that allow parameter studies for different physical parameters. A fully three-dimensional model of a convective stellar wedge will then be used to provide a more detailed picture of the helicity fluxes and their effect on the dynamo. Using recent advancements that allow us to extract surface helicity fluxes from solar observations, the student will make use of observations to verify the simulation results. Other recent observational results on the stellar magnetic helicity will be used to benchmark the findings.


Funded PhD project: Data analytics for urban environmental planning (PhD)

Supervisors: Claire Miller (née Ferguson)
Relevant research groups: Statistics and Data Analytics

The transition to a sustainable society is one of the key challenges facing researchers, policy makers and communities today. Key to future city planning for sustainable solutions is an understanding of what data are available and required to inform effective decision making. Novel data analytics and data visualisations are essential tools in this process. 

This PhD is suitable for someone from a mathematical/computational sciences background with a strong interest in data analytics and data visualisation. This studentship is an opportunity to develop expertise in data-driven analytics/modelling for connecting quantitative and qualitative spatial (and temporal) data streams and investigating questions arising in urban environmental planning. The successful candidate will play a key role within a large, multi-disciplinary project, GALLANT, supporting Glasgow’s sustainable transformation.

You can find futher information here:

Data analytics for urban environmental planning at University of Glasgow on