Postdoctoral Researcher in Causal Inference of Gene Networks

About the role

We are seeking a postdoctoral researcher to develop and apply computational and statistical models that reverse‑engineer causal networks from large‑scale, multi‑modal single‑cell datasets. The position offers high independence, rapid idea testing, and close collaboration within an interdisciplinary team at UMass Chan Medical School.

Key responsibilities

• Design, implement, and scale algorithms to infer multi‑modal, condition‑dependent networks from datasets containing millions of cells and tens of thousands of genes or features.
• Apply these algorithms to existing and newly generated data to uncover biological principles across molecular, cellular, and population levels.
• Develop open‑source, user‑friendly software tools for the research community.
• Disseminate results through peer‑reviewed publications, software packages, and academic presentations.
• Collaborate with group members and external research groups as needed.

Required profile

• Strong quantitative background with a PhD in a quantitative discipline (e.g., computer science, statistics, physics, engineering).
• Curiosity about complex systems and ability to work with high‑dimensional, noisy data.
• No prior biomedical training required.

Required skills

• Machine learning
• Causal inference
• Statistical modeling
• Algorithm development for high‑dimensional data