Post Doc - Open Rank

University of Massachusetts Medical School • Worcester, MA

5d • Onsite

About The Position

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 inferring causal networks from Perturb-seq (interventional single-cell CRISPR screens), mapping dynamic network rewiring from joint scRNA-seq + scATAC-seq, and identifying state-specific causal networks from population-scale scRNA-seq. 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.

Requirements

• Strong quantitative skills
• Curiosity about complex systems

Nice To Haves

• Prior biomedical training is not required

Responsibilities

• Develop accurate and scalable algorithms for inferring multi-modal, condition-dependent networks from datasets with millions of samples (cells) between tens of thousands of nodes (genes and genetic features).
• Apply these algorithms on existing and new datasets to uncover biological principles and insights across molecular, cellular, and population levels.
• Build open-source, user-friendly software tools for the community.
• Disseminate findings through peer-reviewed publications, user-friendly software packages, and academic presentations.
• Collaborate with other group members and research groups as needed.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed