PhD in Mathematical Modelling of Antibiotic Resistance in Bacterial Populations at University of Edinburgh

The University of Edinburgh is offering a PhD position in the Alexander Group, focusing on mathematically modelling the emergence of antibiotic resistance in bacterial populations. This interdisciplinary project aims to address a critical public health challenge by developing mathematical models that describe bacterial population dynamics under antibiotic treatment, incorporating interactions with environmental factors and stochastic effects. The research will utilize ordinary differential equations, stochastic birth-death processes, and computational simulations to predict the probability of resistance emergence and explore optimal antibiotic dosing strategies. The successful candidate will join the Institute of Ecology and Evolution within the School of Biological Sciences, benefiting from strong connections to the mathematical biology community at Edinburgh. The project is suitable for candidates with strong mathematical and computational backgrounds, such as degrees in Mathematics, Physics, or related disciplines with substantial coding experience. While extensive biological knowledge is not required, motivation to learn and engage with biological research is essential. Funding is available through the Darwin Trust scholarships, which cover tuition fees and provide a four-year stipend for international students who are not already graduates of the University of Edinburgh. Additional funding opportunities may be available for UK and international students through various doctoral training programs. Applicants should contact Helen Alexander with a CV and a summary of their academic background and motivation, and apply through the University of Edinburgh's online system and the Darwin Trust website before the deadline of 7 January 2026. This PhD offers the opportunity to gain skills in mathematical modelling, computational methods, data analysis, and interdisciplinary collaboration, contributing to the understanding and mitigation of antibiotic resistance in bacterial populations.