You will work on foundational machine learning challenges, leading projects, and collaborating with other researchers. You will have the possibility to participate in basic, translational science that brings your novel machine learning techniques into impactful biomedical applications.
Machine learning topics of interest include, but are not limited to, probabilistic modeling, representation learning, deep learning, time-series modelling, generative models, integrating multi-modal data, model interpretability, convex and non-convex optimization. These topics are inspired by the challenges posed by biomedical data: high-dimensional, multi-modal datasets with missing data, collected under noisy and imperfect conditions, with complex temporal dynamics and a sensitive nature.
Interested postdoc applicants should have a Ph.D. in machine learning, optimization, or statistics with a strong publication record in top conferences such as NIPS, ICML, ICLR, AISTATS, AAAI, KDD, etc.
Experience working on medical or biological data is not required, but you must bring a keen interest in the problems of the field.
Please send inquiries about the lab and possible projects as well as applications by email to Gunnar Rätsch and use “#application” in the subject line.