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Postdoc In Topological Data Jobs (NOW HIRING)

UMass Med School

Post Doc - Open Rank

Worcester, MA · On-site

$75K/yr

Additional Information Postdoc in Causal Inference of Complex Gene Networks We invite applications ... data. This role offers high independence, rapid idea testing, and close collaboration with an ...

nudge

Human Neuroscientist

San Francisco, CA · On-site

Design, execute, and analyze ultrasound neuromodulation studies in humans * Integrate data from ... A PhD or postdoc in cognitive neuroscience, neuroengineering, or a similar science or engineering ...

The Pennsylvania State University

... topological materials and multiferroics. The project involves bulk single-crystal growth ... D. in physics or a related field, with experience in single-crystal growth and characterization ...

Nudge

Human Neuroscientist

San Francisco, CA · On-site

Design, execute, and analyze ultrasound neuromodulation studies in humans * Integrate data from ... A PhD or postdoc in cognitive neuroscience, neuroengineering, or a similar science or engineering ...

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How much do postdoc in topological data jobs pay per hour?

As of Jul 21, 2026, the average hourly pay for postdoc in topological data in the United States is $33.07, according to ZipRecruiter salary data. Most workers in this role earn between $24.52 and $39.18 per hour, depending on experience, location, and employer.

What is a Postdoc in Topological Data?

A Postdoc in Topological Data is a researcher who has completed their PhD and is engaged in advanced research focused on the application of topology—an area of mathematics dealing with spatial properties—to analyze and interpret complex data sets. These positions typically involve both theoretical work and the development of computational tools to extract meaningful patterns from high-dimensional or complex data. Postdocs in this field often collaborate with interdisciplinary teams in mathematics, computer science, and applied domains such as biology or engineering. The role is intended to deepen expertise, publish research, and prepare for academic or research-intensive careers.

What are some typical collaborative opportunities for a Postdoc in Topological Data within academic or research settings?

As a Postdoc in Topological Data, you can expect to collaborate closely with interdisciplinary teams, including mathematicians, computer scientists, and domain experts from fields like biology or materials science. Collaborative projects often involve developing or applying topological methods to analyze complex datasets, contributing both theoretical insights and computational tools. These interactions may include co-authoring research papers, participating in joint seminars, and working with graduate students. Such collaborations not only broaden your research impact but also help expand your professional network and skill set.

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

To thrive as a Postdoc in Topological Data, you need a strong background in mathematics (particularly topology, algebra, and geometry), data analysis, and a PhD in a related field. Familiarity with computational tools like Python, R, MATLAB, and software libraries for topological data analysis (such as GUDHI or Ripser) is typically required. Exceptional problem-solving ability, collaboration, and strong scientific communication skills help distinguish top candidates in interdisciplinary research environments. These skills are crucial for advancing research, publishing high-quality work, and contributing effectively to collaborative scientific projects.
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Infographic showing various Postdoc In Topological Data job openings in the United States as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $68,795 per year, or $33.1 per hour.
Post Doc - Open Rank

Post Doc - Open Rank

University of Massachusetts Medical School

Worcester, MA • On-site

$62K - $75K/yr

Full-time

Posted 18 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.

Apply