Academic postions

Mitchell Fellowship in Statistics & Data Analytics

The University of Glasgow invites applications for a 3-year Mitchell Fellowship in the School of Mathematics and Statistics. The candidates will undertake independent research in a field of Statistics & Data Analytics (broadly construed), that complements or reinforces the existing research strengths within the School.

Closing date - 22nd October at 11.45pm UK time

To apply please follow this link: Mitchell Fellowship in Statistics & Data Analytics

Postdoctoral postions

Currently no vacancies available

Professional, Administrative and Support opportunities

Leadership Support Assistant

An exciting opportunity has arisen for a Leadership Support Assistant to work within the School of Mathematics and Statistics. In this role, the postholder will provide a broad range of support services to the School contributing to the achievement of the School’s strategic objectives by delivering a proactive, efficient and flexible range of administrative activities

Closing date - 29th October at 11.45pm UK time

To apply please follow this link: Leadership Support Assistant

Learning and Teaching Office Manager

The Learning and Teaching Manager will lead a dynamic team responsible for providing comprehensive administrative support for learning and teaching activities within the School of Mathematics & Statistics within the College of Science and Engineering. This role involves collaboration with the Director of Learning & Teaching, Head of Professional Services, and other stakeholders to ensure the efficient functioning of educational processes. The role requires innovative thinking, resource management, and continuous improvement of administrative procedures to foster an effective learning environment across undergraduate and postgraduate taught programmes.

Closing date - 29th October at 11.45pm UK time

To apply please follow this link: Learning and Teaching Office Manager

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.


Statistical methodology for Assessing the impacts of offshore renewable developments on marine wildlife (PhD)

Supervisors: Janine Illian
Relevant research groups: Statistics and Data Analytics

(jointly supervised by Esther Jones and Adam Butler, BIOSS)

Assessing the impacts of offshore renewable developments on marine wildlife is a critical component of the consenting process. A NERC-funded project, ECOWINGS, will provide a step-change in analysing predator-prey dynamics in the marine environment, collecting data across trophic levels against a backdrop of developing wind farms and climate change. Aerial survey and GPS data from multiple species of seabirds will be collected contemporaneously alongside prey data available over the whole water column from an automated surface vehicle and underwater drone.

These methods of data collection will generate 3D space and time profiles of predators and prey, creating a rich source of information and enormous potential for modelling and interrogation. The data present a unique opportunity for experimental design across a dynamic and changing marine ecosystem, which is heavily influenced by local and global anthropogenic activities. However, these data have complex intrinsic spatio-temporal properties, which are challenging to analyse. Significant statistical methods development could be achieved using this system as a case study, contributing to the scientific knowledge base not only in offshore renewables but more generally in the many circumstances where patchy ecological spatio-temporal data are available. 

This PhD project will develop spatio-temporal modelling methodology that will allow user to anaylse these exciting - and complex - data sets and help inform our knowledge on the impact of off-shore renewable on wildlife.