Genomic surveillance, i.e. using a pathogen's genetic blueprint to track it's spread in response to control measures, has become a key tool in contemporary disease management. This approach can now be deployed almost anywhere and conducted in near real-time to inform rapid and targeted interventions. Yet genomic surveillance is typically applied to emerging diseases with pandemic potential (e.g. SARS-COV-2) in high-income countries. I propose repurposing genomic surveillance for endemic zoonoses, diseases that spread from animals to people, such as rabies, that inflict a major preventable disease burden in many low- and middle-income countries. Such an approach would simultaneously improve surveillance for emerging infectious diseases through locally relevant capacity strengthening and provide the means to develop cutting-edge methodologies and capacities in "pandemic peacetime", whilst generating visible, tangible impacts on public health. My proposal focuses on developing and optimising an accessible toolkit for generating and interpreting rabies virus genetic sequence data, supporting the global strategy to achieve zero human rabies deaths from rabies spread by domestic dogs by 2030. Genomic surveillance can play a key role in resolving complex transmission dynamics, detecting introductions and identifying their sources. This epidemiological understanding is important for both designing and evaluating national rabies elimination programmes, as they are increasingly rolled out. The proposed work encapsulates novel laboratory techniques to generate sequences in resource poor settings, and contemporary methods for analyzing these data to track rabies spread and persistence, both locally within communities and longer-range movement across countries and regions. I will deploy this toolbox through my extensive international collaborations across Africa, Asia and Latin America to address specific questions to support rabies elimination programmes in practice, building local capacity and expertise for routine in-country genomic surveillance.

Parental age effects on offspring quality

Drawing on a decade-long dataset of repeat topographic surveys from 6 rivers across Scotland and North-Western England, this PhD project will offer an opportunity to explore the mid-term impact of river restoration schemes in upland catchments in the UK. Using a comparative approach across catchments which exhibit different hydrological and morphological regimes, the project aims to understand if river restoration projects can be implemented as an effective form of flood management while restoring a more 'natural' hydrological regime to support ecosystem recovery. Using the repeat topographic surveys, the successful candidate will use 2D hydraulic modelling and geomorphic change detection (GCD) software to appraise the river restoration schemes in terms of changes to river morphology and hydraulics. For example, by combining 2D flood model results with GCD output, the candidate can analyse the effect of the river restoration on the distribution of bed shear stress in time and compare outputs to GCD results which highlight regions of geomorphic change. The numerical model will be calibrated against hydrological data from the National River Flow Archive, satellite imagery of historical flood events, and field surveys.

Traditional endoscopes comprise many thousands of optical fibres resulting in sizable systems. Here in Glasgow we are developing an endoscope the width of a single human hair. This new approach gives images of hard-to-reach areas, but sometimes sound is important too. The aim of this project is to extend the functionality of our endoscope to give both images and sound. We will achieve this through high-speed homodyne imaging of the back scattered light. This will involve skills in optical design, algorithm development and high-speed computing. Beyond the technical aspects of the project the student will interact strongly with our external collaborating organisations.

Currently, there is no established methodology for implementing Doughnut Economics that fully accounts for the dynamic interaction between the targets. Researchers in GALLANT are working on it, and this PhD is a part of this activity. On this project, Jonathan is using a mixed-methods approach, combining both qualitative methods (e.g. The Soft Systems Methodology) and quantitative approaches (e.g. The NK fitness models), and applying them using the City of Glasgow as the case study. They are working in a multidisciplinary environment, linking knowledge across healthcare, economics, and environmental systems to support decision-making by Glasgow City Council.