奥地利科学院(奥地利科学院)分子医学的CeMM研究中心生物医学中机器学习博士后

Job Description

We are recruiting a computational postdoc who wants to pursue groundbreaking research on digital medicine, combining a strong background in machine learning with cutting-edge technologies such as single-cell sequencing, wearable devices, ubiquitous sensors, and augmented reality, and a keen interest in biomedical applications.

Our group is based at the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences in Vienna, on the campus of one of the world’s largest hospitals and medical schools. We combine a strong background in computational methods with the expertise, collaborations, and funding to pioneer the use of advanced digital technology in biotechnology and personalized medicine.


The Project

The successful candidate will develop and apply advanced machine learning technology (e.g., deep neural networks, kernel methods, non-linear regression, and/or causal modeling) in order to discover fundamental mechanisms of biology and medicine and to advance personalized medicine. Potential applications may include (but are not limited to) single-cell sequencing of cancer, 3D reconstruction of tumors and epigenetic landscapes, mobile health technology for patients with brain cancer, and pattern discovery in heterogeneous biomedical datasets. Our location on one of the largest medical campuses in Europe ensures direct relevance to medicine, while our close collaboration with the Max Planck Institute for Informatics (Germany) provides first-hand access to a cutting-edge computer science environment.


The Candidate

We are looking for highly motivated and academically outstanding candidates who want to pursue a career in machine learning research and its applications in biology and medicine. Candidate should have a strong background in the quantitative sciences (computer science, bioinformatics, statistics, mathematics physics, engineering, etc.). We will also consider applicants with a background in medicine or in biology (e.g., functional genomics, chemical biology, human genetics, molecular medicine, etc.) who have strong quantitative skills and a keen interest in pursuing computational projects with a major machine learning component.