Internship (with prospect towards a PhD thesis) in statistical data science with medical applications. In the framework of a collaboration between the University of Bern and Inselspital (Bern University Hospital), we are seeking highly qualified, motivated and creative candidates wishing to develop new statistical and machine learning methods for personalized gynecology.

The recruited student(s) will be jointly supervized by Prof. Dr. David Ginsbourger (Statistics) and PD Dr. Ben Spycher (Epidemiology) and closely collaborate with the teams of Prof. Dr. Petra Stute (Inselspital) and Dr. Rowan Iskandar (SITEM Insel) as well as further partners of their joint project funded by the newly created Multidisciplinary Center for Infectious Diseases (MCID). There will also be interactions with a related project in the framework of the Center of Artificial Intelligence for Medecine (CAIM). The key goal of these projects is to develop a digital medical device App for women in or after menopause. The App will work with data from a smart tracker to drive statistical machine learning models to produce personalized risk assessments of chronic disease development and will issue early warnings (red flags) of potential respiratory tract infections (e.g. common colds, flu, COVID-19), ideally before symptoms begin.

The ideal candidates will have recently earned or be about to finish their master’s degree in a subject with a strong mathematical component (e.g. mathematics, statistics, physics or similar), a genuine interest in statistical data science and applications thereof, a taste for both theoretical investigations and numerical experiments, and good programming skills. Beyond preliminary data handling, analysis and modelling tasks involved in the initial phase (internship), the goal will be to develop and implement inhomogeneous point process models for infectious disease warnings based on various data sources including data-streams from mobile phones and wearables, medical test results, questionnaire responses, and external information such as infection rates of viruses circulating in the population.

Applications should be sent to and