PhD Position in Statistical Data Science with Medical Applications

University of Bern Department of Mathematics and Statistics

PhD Position 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 innovative statistical & machine learning methods in the area of personalized health.

The recruited student 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 (Endocrinological gynecology, Inselspital) and Dr. Rowan Iskandar (Health economics, SITEM Insel) within 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).

Tasks
The overarching goal is to help developing 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. Beyond preliminary data handling, analysis and modelling tasks involved in the initial phase, the target will be to develop and implement prediction and warning methods relying on latent models such as 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. This diversity of data types will call for creative solutions that may encompass investigating novel classes of kernels.

Requirements
The ideal candidate will have recently earned or be about to finish their master's degree in statistics or neighbouring subjects with a strong mathematical component, a genuine interest in statistical data science and applications thereof, a taste for both theoretical investigations and numerical experiments, and solid programming skills.

We offer
The salary will be at the level foreseen by the SNSF. There might be a possibility to complement funding by taking up teaching and consulting duties. The funding is secured for 3 years with the starting date of September 1st 2022 or as can be arranged by mutual agreement.
 
Applications should contain: (1) a letter in which the applicants describe their research interests and the motivation to apply, (2) a complete CV, (3) copies of relevant diplomas, certificates as well as the full transcript of records, (4) an electronic version of a research work (Master thesis or other scientific publication), (5) contact information of 2 – 3 references.

Applications and inquiries should be sent to both Prof. Dr. David Ginsbourger and PD. Dr. Ben Spycher at david.ginsbourger@stat.unibe.ch and ben.spycher@ispm.unibe.ch