Magda Bienko

Uppsala University is seeking a postdoctoral researcher in Machine Learning with a strong interest in the organization of life, specifically focusing on the 3D structure and dynamics of chromosomes. The position is part of the NEST project, 'Learning 3D Genome Dynamics from Heterogeneous Data,' funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP) and the Wallenberg National Program for Data-Driven Life Science (DDLS). The project aims to develop and apply advanced machine learning models to understand how DNA is organized within cells and how this organization changes over time, with a particular focus on the model organism Escherichia coli. The research will involve generating and analyzing large-scale single-cell datasets to map 3D genome structures and their dynamics, integrating data from multiple experimental approaches. The successful candidate will work closely with leading research groups, including those led by Thomas Schön (Uppsala University), Johan Elf (Uppsala University), and Magda Bienko (Karolinska Institutet), combining expertise in artificial intelligence, bioinformatics, and molecular biology. Key research areas include machine learning, spatiotemporal modeling, bioinformatics, chromosome structure, and data-driven life science. The project offers a unique opportunity to contribute to fundamental discoveries in genome organization and to develop computational tools with broad applications in biology and synthetic genomics. Applicants should have a PhD in computer science, machine learning, computational biology, bioinformatics, or a related field, with experience in machine learning and biological data analysis. The position is fully funded for up to 5 years, with support from major Swedish research programs. The application deadline is January 23, 2026. For more information and to apply, visit the official advertisement link.