Academic postions

Lecturer/Senior Lecturer/Reader

The School of Mathematics & Statistics is looking for enthusiastic candidates to join the rapidly and substantially growing Statistics and Data Analytics group, and to further enhance and diversify staff interests and expertise within the group. These positions are a result of strategic investment into our AI3 initiative of Analytics and Inference Innovation for Impact, for more information see  This initiative combines our existing success across research and teaching innovation into a centre of excellence in the areas of statistics, data analytics and applied data science.

Applications for these posts are welcome in any areas of data analytics, data science, statistical methodology and teaching innovation that complement the existing strengths within the School.  We particularly encourage applications at senior/leadership levels (grade 9) and from those with interests at the interface of computational statistics and data science that could contribute/lead related research or scholarship within the School, and contribute to our very successful and expanding online distance learning and on-campus MSc programmes in Data Analytics.

For more information on the vacancies and the School itself, please see our candidate flipbook: and visit the vacancy posting on the University of Glasgow vacancies website

Closing date for applications is 25th January 2021.

Postdoctoral postions

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

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

Digital Catchment Twins (DigiCaT): to protect environmental flows in Scottish rivers 

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:, 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 - Modelling and Analysis of Material Systems, Mathematical Biology, Statistics and Data Analytics, Continuum Mechanics - Fluid Dynamics and Magnetohydrodynamics

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)

Supervisors: Dirk Husmeier, Hao Gao, Xiaoyu Luo
Relevant research groups: Mathematical Biology, Statistics and Data Analytics

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.


Topological full groups and continuous orbit equivalence (PhD)

Supervisors: Xin Li
Relevant research groups: Geometry and Topology, Analysis, Algebra

This proposed PhD project is part of a research programme whose aim is to develop connections between C*-algebras, topological dynamics and geometric group theory which emerged recently.

More specifically, the main goal of this project is to study topological full groups, which are in many cases complete invariants for topological dynamical systems up to continuous orbit equivalence. Topological full groups have been the basis for spectacular developments recently since they led to first examples of groups with certain approximation properties, solving long-standing open questions in group theory. The goal of this project would be to systematically study algebraic and analytic properties of topological full groups. This is related to algebraic and analytic properties of topological groupoids, the latter being a unifying theme in topological dynamics and operator algebras. A better understanding of the general construction of topological full group -- which has the potential of solving deep open questions in group theory and dynamics -- goes hand in hand with the study of concrete examples, which arise from a rich variety of sources, for instance from symbolic dynamics, group theory, semigroup theory or number theory.

Another goal of this project is to develop a better understanding of the closely related concept of continuous orbit equivalence for topological dynamical systems. This new notion has not been studied in detail before, and there are many interesting and important questions which are not well-understood, for instance rigidity phenomena. Apart from being interesting on its own right from the point of view of dynamics, the concept of continuous orbit equivalence is also closely related to Cartan subalgebras in C*-algebras and the notion of quasi-isometry in geometric group theory. Hence we expect that progress made in the context of this project will have an important impact on establishing a fruitful interplay between C*-algebras, topological dynamics and the geometry of groups.

The theme of this research project has the potential of shedding some light on long-standing open problems. At the same time, it leads to many interesting and feasible research problems.


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 is part of the LifETIME Centre for Doctoral Training

and involves collaboration with Prof Matt Dalby (Institute of Molecular, Cell and Systems Biology) and industrial partner Cell Guidance Systems. Applicants must apply to the CDT (details on the website) to be considered for this project.