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Causal Inference Postdoc Jobs (NOW HIRING)

... Postdoc Department Shift Weekday Day Position Type Full Time Scheduled Weekly Hours 40 JR7572 We ... Causal Inference, External Controls, and Real-World Evidence Development of principled approaches ...

Postdoctoral Research Associate

Boston, MA ยท Remote

$60K - $85K/yr

Applying epidemiologic, econometric, and other methods to strengthen causal inference * Working ... Exploring gender, racial/ethnic, and socioeconomic disparities The Postdoctoral Research Associate ...

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How much do causal inference postdoc jobs pay per year?

As of Sep 14, 2026, the average yearly pay for causal inference postdoc in the United States is $242,952.00, according to ZipRecruiter salary data. Most workers in this role earn between $250,000.00 and $250,000.00 per year, depending on experience, location, and employer.

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Infographic showing various Causal Inference Postdoc job openings in the United States as of August 2026, with employment types broken down into 80% Full Time, 19% Part Time, and 1% Contract. Highlights an 68% Physical, 3% Hybrid, and 29% Remote job distribution, with an average salary of $242,952 per year, or $116.8 per hour.

Post Doc - Open Rank

Worcester, MA โ€ข On-site

University of Massachusetts Medical School
Educationย โ€ขย 10K+ employees

$62K - $75K/yr

Full-time

Re-posted 13 days ago


Job description

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 requiredโ€”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.