Post Doc - Open Rank (2026-49874)

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When you join the UMass Chan Medical School team, you join us in advancing together to improve the health and well-being of our diverse communities throughout Massachusetts and across the US and the world. Together, we lead in education, research, health care delivery, and public service. Your life's work is more than a career. It's an expression of your passion, intellect, skill, and drive. UMass Chan"s commitment to excellence, innovation, competitive benefits, and work-life integration will allow you to build a professionally rewarding career as we work together to better or improve the health of people around the globe.

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Post Doc - Open Rank

Job Number: 2026-49874

Category: Research

Location: Worcester, MA

Shift: Day

Exempt/Non-Exempt: Exempt

Business Unit: UMass Chan Medical School

Department: School - Genomics and Computational Biology - W403700

Job Type: Full-Time

Union Code: Non Union Position-W63-Residents/Post Docs

Num. Openings: 1

Post Date: May 11, 2026

Work Location: Hybrid

Salary Minimum: USD $62,232.00/Yr.

Salary Maximum: USD $75,564.00/Yr.

Postdoc in Causal Inference of Complex Gene Networks

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.
• 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.

Position Overview

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.

Key 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.

Posting Disclaimer:

This job posting outlines the primary responsibilities and qualifications for the role but is not intended to be an exhaustive list. Duties and expectations may evolve in response to the needs of the department and the broader institution.

In alignment with our commitment to pay transparency, the base salary range for this position is listed above (exclusive of benefits and retirement). At UMass Chan Medical School, final base salary offers are determined based on a combination of factors, including your skills, education, and relevant experience. We also consider internal equity to ensure fair and consistent compensation across our teams.

Please note that the range provided reflects the full base salary range for this position. Offers are typically made within the midrange to allow for future growth and development within the role.

In addition to base pay, UMass Chan offers a comprehensive Total Rewards package, which includes paid time off, medical, dental, and vision coverage, and participation in a 401(a)-retirement plan, with the option to contribute to a voluntary 403(b) plan.

UMass Chan welcomes all qualified applicants and complies with all state and federal anti-discrimination laws.

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