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Postdoc In Network Science Jobs (NOW HIRING)

SRI International

Postdoc, Formulation Science

Menlo Park, CA · On-site

$69.89K - $8.74M/yr

The Postdoc position will work onsite in our Menlo Park, CA office. Responsibilities * Development ... Report and present scientific/technical results internally and to external clients * Contribute to ...

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$38.5K

$66.8K

$121K

How much do postdoc in network science jobs pay per year?

As of May 31, 2026, the average yearly pay for postdoc in network science in the United States is $66,802.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $77,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Postdoc in Network Science, and why are they important?

To thrive as a Postdoc in Network Science, you need advanced expertise in mathematics, statistics, and network theory, typically supported by a PhD in a related field. Proficiency with programming languages such as Python or R, experience using network analysis tools (e.g., Gephi, NetworkX), and familiarity with data visualization and machine learning libraries are highly valued. Strong analytical thinking, effective scientific communication, and collaborative skills help you excel in interdisciplinary research environments. These competencies are crucial for producing high-impact research, advancing scientific understanding, and contributing meaningfully to collaborative projects.

What are the typical collaborative opportunities for a Postdoc in Network Science within academic and interdisciplinary research teams?

As a Postdoc in Network Science, you will often work closely with researchers from various disciplines such as computer science, physics, biology, and social sciences. Collaboration is a key aspect of this role, as many projects require expertise in both network theory and domain-specific knowledge. You may participate in joint publications, grant proposals, and interdisciplinary workshops, which not only broaden your research impact but also help you build a diverse professional network. This collaborative environment fosters innovative approaches and can open doors to future academic or industry positions.

What is a Postdoc in Network Science?

A Postdoc in Network Science is a researcher who has completed their PhD and is engaged in postdoctoral research focused on the study of complex networks. This includes analyzing and modeling the interconnectedness of systems ranging from social networks to biological and technological networks. Postdocs in this field typically work at universities or research institutes, contributing to scientific projects, publishing research, and sometimes mentoring students. Their work often involves interdisciplinary collaboration, utilizing tools from mathematics, computer science, and physics. The position is usually temporary and intended to further develop research skills and expertise before moving into longer-term academic or industry roles.
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Postdoc In Network Science jobs near you
Infographic showing various Postdoc In Network Science job openings in the United States as of May 2026, with employment types broken down into 92% Full Time, and 8% Part Time. Highlights an 92% In-person, and 8% Remote job distribution, with an average salary of $66,802 per year, or $32.1 per hour.
Post Doc - Open Rank

Post Doc - Open Rank

University of Massachusetts Medical School

Worcester, MA

$62.23K - $75.56K/yr

Full-time

Posted 27 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 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.

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