PhD Student (gn*) Pharmacoepidemiology

You will be in charge of a work package of the project. You will prepare and register study protocols, perform data management of electronic health records, implement the pre-specified new/prevalent user or target trial designs, perform statistical analyses (such as inverse probability weighting and instrumental variable estimation), attend training activities, present findings in team/collaborator meetings and at international conferences, and write and publish scientific papers. After the three-year funding period, you will have published several impactful studies in the peer-reviewed literature, submitted your thesis, and successfully defended your PhD. The development of a personal fellowship and grant applications will be encouraged.

• We are interested in recruiting a motivated individual with an academic degree (Master’s) in epidemiology, statistics, mathematics, or pharmacy. However, we have an open mind regarding the skills profile of a successful candidate for this position. Most importantly, we seek an inquisitive person and the desire to adapt epidemiological methodology to questions of substantive clinical relevance. Experience in applying epidemiological designs and statistical methods to large observational data is important. An understanding of study design and causal inference in the field of pharmacoepidemiology is essential. Given the large datasets, computational skills are fundamental.
• The ideal candidate should have:
• A strong background in pharmacoepidemiology and causal inference with interest in applications
• Good programming skills (Linux, R preferred, Python is a plus)
• Experience in addressing drug safety issues using large claims or electronic health records
• Good communication skills and excellent command of English (German is a plus)

• Support for further personal development and training (e.g. structured development of leadership competencies)
• An interesting and versatile project with challenging tasks
• Access to the latest computing infrastructure
• Training and work in a multidisciplinary and committed research team

Please note that, while we can be flexible, some level of in-person work will be required; it will not be possible to undertake this position fully remotely.

Applications of women are specifically invited. In the case of similar qualifications, competence, and specific achievements, women will be considered on preferential terms within the framework of the legal possibilities. Handicapped candidates with equivalent qualifications will be given preference.