PhD Student in Forecasting Resistance Spread and Epidemiological Impact

Drive your own PhD project on developing mechanistic/process-based, spatially explicit models of insecticide resistance spread and impact, using Bayesian statistics, mathematical modelling, machine learning, and quantitative genetics. Implement resistance-spread models using state-of-the-art programming practices with version control; calibrate them to available data; integrate them with our epidemiological simulation platform; and evaluate resistance-management strategies to maintain insecticide effectiveness. Collaborate closely with project partners, including statisticians, machine-learning researchers, entomologists, and disease modellers at Oxford University, Imperial College London, Wageningen University & Research, Università della Svizzera italiana, and Swiss TPH. Communicate your research through peer-reviewed publications, conference presentations, workshops, and regular meetings with collaborators.

The Swiss Tropical and Public Health Institute (Swiss TPH) is a world-leading institute in global health with a particular focus on low- and middle-income countries. Associated with the University of Basel, Swiss TPH combines research, education and services at local, national and international levels. 1'000 people from 96 nations work at Swiss TPH focusing on infectious and non-communicable diseases, environment, society and health as well as health systems and interventions.

• MSc degree with excellent grades in a quantitative field (e.g. statistics, mathematics, computer science, physics) or in biology/environmental sciences/epidemiology with rigorous quantitative training

• Strong analytical skills for developing mathematical/statistical methods and strong programming skills for implementing them. Experience in R, Python, Stan, HPC environments, and Git is an advantage

• A demonstrated interest or strong enthusiasm for delving into Bayesian statistics, modelling, resistance evolution, and public health

• Ideally, prior experience in Bayesian data analysis, spatial statistics, evolutionary biology, computational biology, disease modelling, environmental modelling, analysis of genetic/genomic data, or quantitative genetics

• Ability to manage your work independently and collaboratively, including planning, documenting, and communicating your work effectively

• Ability to communicate research effectively in spoken and written English