Rankin-Sneddon Research Fellowships
The School of Mathematics and Statistics at the University of Glasgow invites applications for two 3-year Rankin-Sneddon Research Fellowships in the School of Mathematics and Statistics. The candidates will undertake independent research in a field of mathematics (pure or applied) that complements or reinforces the existing research strengths within the School. While primarily a research position, this fellowship will carry a light teaching load, which will normally mean lecturing one 20-credit course, or two 10-credit courses, plus tutorials, each academic year. This will typically translate into approximately 4 contact hours per week in each of two 11-week semesters per year, the distribution of which may be flexible. Research fellows will also have the opportunity to supervise Honours and/or Masters Projects in their area of interest.
For further information please visit
Closing date for applications - 5pm, Friday 19th February 2021
Currently no vacancies available
Professional, Administrative and Support opportunities
Currently no vacancies available
Funded Ph.D opportunities
Iapetus PhD studentships
Water, Climate and Development: Google Earth Engine for water resources
* Glasgow/Stirling: (Spyrakos, O'Donnell, Tyler, Hunter)
A tale of two lagoons: Determining the drivers and trajectories of change for the Venice (Italy) and Razelm-Sinoe (Danube-Delta, Romania) lagoons through Earth observation and modelling
* Glasgow/Stirling: (Tyler, Miller, Spyrakos, O'Donnell, Hunter, Scott)
Also see http://www.iapetus.ac.uk/studentships.
Digital Catchment Twins (DigiCaT): to protect environmental flows in Scottish rivers
Supervisors: Professor Marian Scott, University of Glasgow; Dr Kit Macleod, James Hutton Institute; Professor Claire Miller, University of Glasgow; Dr Andrew Elliott, University of Glasgow; Dr Matt Aikenhead, James Hutton Institute
About the Project
Innovations in digital technologies are enabling support of near real-time catchment scale decision making by farmers and other stakeholders. These innovations include IoT (Internet of Things) advances in low cost and low power in-situ sensors (e.g. sensing water quantity, soil moisture, and atmospheric conditions, as well as cameras); on-device analytics and machine learning; web/mobile dashboards that integrate multiple data sources to aid individual and collective decision making. The project will explore how catchment scale IoT technologies can aid near-real-time catchment water resource information that is available to all stakeholders to support improvements in the collaborative use of water resources to protect environmental flows in Scottish rivers. The scholar will work with colleagues and wider stakeholders including farmers to integrate a range of IoT and other (including satellite earth observation) data streams to understand the status of catchment water flows (including inputs and outputs, e.g. water abstractions) to protect environmental flows. The approaches developed will be scaleable and transferable to other catchments. The scholar will develop and apply analytic tools using a variety of statistical and computational approaches including network models, machine learning including Long Short-Term Memory networks and spatio-temporal statistical data fusion to provide near-real-time predictions and uncertainties on water quantity (and quality) across the river network.
Applicants are strongly advised to make an informal enquiry about the PhD to the primary supervisor well before the final submission deadline. Applicants must send a completed application form (available here: https://www.hydronationscholars.scot/apply), their Curriculum Vitae and a covering letter to the primary supervisor by the final submission deadline of 8th January 2021.
Predicting patterns of retinal haemorrhage (PhD)
Supervisors: Peter Stewart
Relevant research groups: Continuum Mechanics - Fluid Dynamics and Magnetohydrodynamics, Continuum Mechanics - Modelling and Analysis of Material Systems, Mathematical Biology, Statistics and Data Analytics
Retinal haemorrhage (bleeding of the blood vessels in the retina) often accompanies traumatic brain injury and is one of the clinical indicators of `shaken baby syndrome'. This PhD project will give you the opportunity to develop a combination of mathematical and statistical models to help explain the onset of retinal haemorrhage. You will devise and implement image processing algorithms to quantify the pattern of bleeding in clinical images of haemorrhaged retinas. In addition, you will develop a mathematical model for pressure wave propagation through the retinal circulation in response to an acute rise in intracranial pressure, to predict the pattern of retinal bleeding and correlate to the images. Cutting-edge pattern recognition methods from Machine Learning and Bayesian modelling will be used to infer characteristic signatures of different types of brain trauma. These will be used to help clinicians in characterising the origin of traumatic brain injury and diagnosing its severity. This is an ideal project for a postgraduate student with an interest in applying mathematical modelling, image analysis and machine learning to predictive healthcare. The project will give you the opportunity to join a cross-disciplinary Research Hub that aims to push the boundaries of quantitative medicine and improve clinical decision making in cases of suspected non-accidental head injury using innovative mathematical and statistical modelling.
Assessing risk of heart failure with cardiac modelling and statistical inference (PhD)
In recent years, we have witnessed impressive developments in the mathematical modelling of complex physiological systems. However, parameter estimation and uncertainty quantification still remain challenging. This PhD project will give you the opportunity to join an interdisciplinary research team to develop new methodologies for computational modelling and inference in cardio-mechanic models. Your ultimate objective will be to contribute to paving the path to a new generation of clinical decision support systems for cardiac disease risk assessment based on complex mathematical-physiological models. You will aim to achieve patient-specific calibration of these models in real time, using magnetic resonance imaging data. Sound uncertainty quantification for informed risk assessment will be paramount. This is an ideal PhD project for a postgraduate student with a strong applied mathematics and statistics or Computer Science background who is interested in computational mechanics and adapting cutting-edge inference and pattern recognition methods from Machine Learning and Bayesian modelling to challenging cardio-mechanic modelling problems. The project will give you the opportunity to join a cross-disciplinary Research Hub that aims to push the boundaries of quantitative medicine and improve cardio-vascular healthcare by bringing cutting-edge mathematical and statistical modelling into the clinic.
Theoretical modelling of cell response to external cues (PhD)
Supervisors: Peter Stewart
Relevant research groups: Continuum Mechanics - Fluid Dynamics and Magnetohydrodynamics, Continuum Mechanics - Modelling and Analysis of Material Systems, Mathematical Biology
Cells and tissues respond to mechanotransductive and biochemical cues. These external cues interact with protein signaling pathways within the cell and can trigger changes in size, structure, binding and differentiation. This project will use theoretical modelling to examine the response of an array of cells to various external mechanical and biochemical cues, considering how these cues can be tailored to optimize a particular outcome. The model will couple the mechanical components of the cell (nucleus, cytoskeleton,…) to internal protein expression pathways (Myosin II, MLCK,…) and the properties of the external stimuli. The model will take the form of coupled differential equations which will be solved using both analytical and numerical methods.
This model will be validated against experimental data in two main ways, including examining the response of the array to small amplitude mechanical vibration (‘nanokicking’) to predict its influence on the behavior of the array over long timescales. The model will also be used to understand growth factor delivery using PODS® technology developed by Cell Guidance Systems to predict the optimal spatial arrangement of PODS® relative to the array and the resulting temporal and spatial profiles of both the growth factor and the cell growth and proliferation.
This project involves collaboration with Prof Matt Dalby (Institute of Molecular, Cell and Systems Biology).
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.