PpbYour position /b /p /p bYour various responsibilities include: /bbr/liDrive 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 /liliImplement 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 /liliCollaborate 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 /liliCommunicate your research through peer-reviewed publications, conference presentations, workshops, and regular meetings with collaborators /li ppbbYour profile /b /b /p /p bYou should have the following experience and skills: /bbr/liMSc 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 /liliStrong 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 /liliA demonstrated interest or strong enthusiasm for delving into Bayesian statistics, modelling, resistance evolution, and public health /liliIdeally, prior experience in Bayesian data analysis, spatial statistics, evolutionary biology, computational biology, disease modelling, environmental modelling, analysis of genetic/genomic data, or quantitative genetics /liliAbility to manage your work independently and collaboratively, including planning, documenting, and communicating your work effectively /liliAbility to communicate research effectively in spoken and written English /li