Postdoc: Causal Multi-Omic Gene Networks

University of Massachusetts Chan Medical School; Worcester (MA); Hybrid; USD 52,000 - 78,000; Full time

Benefits offered by this job

Hybrid-work flexibility

Job summary

UMass Chan Medical School's computational lab invites applications for a postdoctoral researcher position. The lab develops computational methods to reconstruct multi-omic causal gene regulatory networks from large-scale single-cell datasets, combining interpretable machine learning, statistics, and causal inference.

The successful candidate will lead an independent project applying new statistical models to uncover interactions among genes, neurons, microbial species, or other components, with

Qualifications

• Ph.D. in a quantitative or biomedical discipline (completed or expected).
• Strong motivation, curiosity, and scientific rigor; track record of peer-reviewed publications.

Responsibilities

• Develop accurate, scalable methods to infer single-cell multi-omic causal GRNs across large datasets.
• Apply methods to existing and emerging datasets to generate biological insights at multiple scales.
• Disseminate research findings through publications, software packages, and conferences.
• Collaborate with lab members and external partners.
• Contribute to the growth and intellectual development of an interdisciplinary lab.

Education

Ph.D. in a quantitative or biomedical discipline

Tools

Python

R

Julia

MATLAB

Job description

UMass Chan Medical School's computational lab invites applications for a postdoctoral researcher position. The lab develops computational methods to reconstruct multi-omic causal gene regulatory networks from large-scale single-cell datasets, combining interpretable machine learning, statistics, and causal inference.

The successful candidate will lead an independent project applying new statistical models to uncover interactions among genes, neurons, microbial species, or other components, with