Postdoc Funke Group
Pubblicato il 12-09-2026 - Human Technopole in Italia
Postdoc | Human Technopole, Milan
Build the science that shapes the future of human health.
Application closing date: 10.10.2026
Join a place where ambitious science thrives
Human Technopole (Milan) is a rapidly expanding life science institute where international researchers and cutting-edge technologies converge to accelerate biomedical discovery. Our mission is to transform bold scientific ideas into advances that improve human health.
In this context, the Funke lab develops machine learning methods to accelerate scientific discovery with a focus on microscopy image analysis. Specifically, we are interested in the development of new methods to identify structures of interest in large datasets (detection, segmentation, and tracking), the creation of explainable AI methods to interrogate scientific datasets, and the design and implementation of mechanistic models of biological processes that work together with contemporary machine learning methods.
Many life science projects involve the acquisition of time series microscopy data to visualize the dynamics that underline cell proliferation and cell fate decisions. To analyze those datasets and gain novel insights, cells need to be reliably tracked over long periods of time; including the detection of cell division events to create comprehensive lineage trees. Although cell tracking using deep learning methods has been shown to deliver excellent results on selected datasets, experimentalists still struggle to track cells automatically on new datasets: the diversity of possible live-cell datasets makes it hard for a given method to generalize due to differences in fluorescent labeling, resolution, model organism, or exact imaging modality to name a few. No method exists as of now that can reliably handle all of those different conditions.
We are looking for an ambitious Postdoc who will lead our efforts on the design, implementation, and training of active learning strategies to fine-tune a pre-trained cell tracking n