We invite applications for a NIH-funded postdoctoral researcher position in our computational lab at UMass Chan Medical School. We develop methods to reconstruct multi-modal causal networks that govern cellular behavior from large-scale single-cell datasets Our group has pioneered computational approaches for:
We approach single-cell biology as a high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms No prior biomedical training is required—just strong quantitative skills and curiosity about complex systems.
You will design, implement, and apply new computational and statistical models to reverse-engineer causal networks from noisy, high-dimensional, multi-modal data. This role offers high independence, rapid idea testing, and close collaboration with an interdisciplinary team.
If you are excited about tackling problems in complex networks, causal inference, and high-dimensional systems, and applying them to understand how molecular interactions drive cell states and transitions, this is an excellent fit.
