Job description
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 in real time, specialized mathematical and statistical modeling tools are necessary.
The proposed project is focused on the development of new statistical methods to generate, evaluate and combine infectious disease nowcasts and short-term forecasts. Besides statistical prediction methods, it is planned to work on expert and crowd forecasts of disease spread.
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 Information and Data Science School for Health, and we intend to associate doctoral candidates with this graduate school.
Starting date
Early 2024 with some flexibility
Personal qualification
1. Master's degree in statistics / biostatistics, mathematics or epidemiology (with a focus on statistics). A degree in another quantitative discipline (e.g., computer science, social sciences, economics) may be suitable in combination with excellent knowledge in statistics, proven by past coursework.
2. Strong interest in statistical modeling, computational statistics, survey methods and applications in medicine and public health.
3. Programming skills in R or Python, documented by past projects (e.g., public code repositories).
4. Fluency in written and spoken English; English is the main working language.
5. Strong commitment to research ethics and teamwork.
6. Good communication skills.
Salary
Salary category 13 TV-L, depending on the fulfillment of professional and personal requirements.