PhD Student in Forecasting Resistance Spread and Epidemiological Impact

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.

Malaria, transmitted by Anopheles mosquitoes, caused over a million deaths annually in the early 2000s. Insecticide-treated bed nets have since helped halve global mortality, but this progress is threatened by rising insecticide resistance. We build quantitative, data-driven models to forecast the spread and impact of resistance, guiding the optimal use of new insecticide-treated nets.

• 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
• Communicate your research through peer-reviewed publications, conference presentations, workshops, and regular meetings with collaborators

• MSc degree (or nearing completion) with excellent grades in a quantitative field (e.g. statistics, mathematics, computer science, physics) or in biology/environmental sciences/epidemiology with strong 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 management, evolutionary processes, 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

• Direct and dedicated supervision led by Dr. Adrian Denz, with a strong commitment to supporting you in achieving research excellence in Bayesian forecasting of resistance dynamics as well as in developing your academic writing skills and scientific independence
• Close exchange with renowned malaria researchers at Swiss TPH as well as international project partners
• A multidisciplinary, international, and agile research institution with strong links to international policy makers, national health ministries, and industry, and excellent computational infrastructure with access to the HPC cluster at the University of Basel, alongside an active student community
• A state-of-the-art workspace in the new Swiss TPH building with an on-site canteen, located on the dynamic Base Link site in Allschwil near Basel. Basel is a walkable, bikeable city with high quality of life bordering France and Germany, and an international hub for pharmaceutical research and culture.
• Enrollment as a PhD student at the University of Basel, with access to university courses, Swiss Institute of Bioinformatics training, continuing education opportunities, and university sports facilities
• Internationally competitive salary and benefits according to SNSF and University of Basel regulations, fully funded for 3 years via an SNSF Ambizione grant

• Start Date:
  
  as soon as mutually agreed, between March and October 2026
• Duration: 3 years
• Percentage: 100%, primarily…