Infectious disease outbreaks pose a threat to public health and can have disruptive effects on societies. Disease surveillance gives rise to diverse and continuously updated data streams. To draw valid conclusions from these, specialized mathematical and statistical modeling tools are necessary.

The proposed project is focused on the development of new statistical methods to analyze infectious disease data in real time. Possible topics include nowcasting, short-term forecasting and the estimation of reproductive numbers. Besides statistical prediction methods, it is planned to work on expert and crowd forecasts of disease spread as well as evaluation methods for such forecasts.

The position will be part of a DFG Emmy Noether Junior Researcher Group (“Multi-Model Nowcasting and Short-Term Forecasting of Infectious Disease Spread”). For parts of the project, collaborations with partners in the United Kingdom and the United States are intended.

The position is suitable for PhD studies. The planned PhD supervisors are Principal Investigators in the Helmholtz Information and Data Science School for Health, and we intend to associate doctoral candidates with this graduate school.

• Master's degree in statistics / biostatistics, mathematics or data science. A degree in a related quantitative discipline (e.g., epidemiology, economics) may be suitable in combination with excellent knowledge in statistics, proven by past coursework.
• Strong interest in statistical modeling and software as well as applications in medicine and public health. Prior experience with survey methods is an asset.
• Programming skills in R, documented by past projects (e.g., links to public code repositories). Knowledge of Python is an asset.
• Fluency in written and spoken English; English is the main working language. Some knowledge of German is an asset, but no requirement.
• Strong commitment to research ethics and teamwork.
• Good communication skills.