Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology

This postdoctoral position focuses on integrating and analyzing multi-modal single-cell genomic datasets using machine learning to understand cell fate decisions in developmental biology. Applicants must have a strong background in computational biology or machine learning, with proven expertise in single-cell data analysis and proficiency in Python or R. Experience with multi-modal data integration and trajectory inference is highly valued.

• Analysis and publication of results
• Build a strong network in the field of research
• Participate in education, and PhD and master student supervision
• Working and collaborating on research projects
• Strong background in computational biology, bioinformatics, machine learning, or a related quantitative field
• Experience with machine learning and/or statistical modeling applied to biological data
• Proven expertise in single-cell data analysis (scRNA-seq and/or scATAC-seq)
• Interest or experience in multi-modal data integration and trajectory inference
• Proficiency in Python and/or R
• Interest in developmental and human biology is highly valued, but not required
• Strong collaborative mindset and ability to work across disciplines
• A highly interdisciplinary research environment at the interface of ML and biology
• Access to large, state-of-the-art single-cell multi-omics datasets
• Close collaboration with leading groups in single-cell genomics and machine learning
• Strong support for high-impact publications and career development
• Contract Start Date : 06/01/2026