Postdoctoral Researcher in Machine Learning (100%) in Zürich

ETH Zurich is one of the world’s leading universities specialising in science and technology. It is renowned for its excellent education, its cutting-edge fundamental research and its efforts to put new knowledge and innovations directly into practice. The interdisciplinary research group develops and applies advanced methodology to mine large volumes of clinical and medical data to understand and model diseases and treatments. This includes for example the development of new modeling frameworks for the analysis of big, diverse, and longitudinal data sets. We are looking for a

The newly established research group headed by Professor Julia Vogt at ETH Zurich is seeking a highly motivated postdoctoral researcher with a strong machine learning background. Applicants should be interested in working in an interdisciplinary environment at the interface of Machine Learning, Medicine and Health Care. The postdoctoral researcher will interact and collaborate closely with both faculty and graduate students in our research group, as well as with interdisciplinary collaborators from the medical domain. He/she will work on foundational machine learning challenges and will lead projects. The postdoc will have the possibility to participate in basic, translational science that brings novel machine learning techniques into impactful biomedical applications. Machine learning topics of interest include, but are not limited to, probabilistic modeling, deep learning, time-series modelling, generative models, natural language processing and integrating multi-modal data. Our group conducts research at the intersection of machine learning and healthcare, and our research topics are inspired by the challenges which arise by dealing with medical data.

We look forward to receiving your online application including the following documents: a research proposal, a CV and a cover letter / personal statement including the names of three referees. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.