Postdoc in Biological Causal Networks

We invite applications for a postdoctoral researcher position in our computational lab at UMass Chan Medical School. Our lab develops computational methods for reconstructing multi-omic causal gene regulatory networks (GRNs) from large-scale single-cell datasets. Our interdisciplinary research combines interpretable machine learning, statistics, algorithm development, causal inference, and single-cell multi-omics. We aim to advance causal GRN inference while extending our research to other biological network systems, such as neuronal networks and microbial interaction networks.

Position Overview

The successful candidate will lead an independent research project focused on developing and applying cutting-edge statistical models and computational methods to uncover interactions among genes, neurons, microbial species, or other components of complex biological systems. Projects may involve observational, interventional, genetic, temporal, or multi-omic data. Candidates are equally welcome to pursue their own ideas within the lab’s broader research themes or to develop one of our emerging projects in causal GRN inference and analysis. This position is ideal for researchers who are motivated to uncover the organizing principles and complex interactions underlying high-dimensional biological systems.

Key Responsibilities

• Develop accurate, scalable, and computationally efficient methods to infer single-cell multi-omic causal GRNs across millions of cells and tens of thousands of genes or causal networks in other biological systems.
• Apply newly developed methods to existing and emerging datasets to generate biological insights at molecular, cellular, organismal, and population scales.
• Disseminate research findings through peer-reviewed publications, user-friendly software packages, and presentations at scientific conferences.
• Collaborate with other lab members and external collaborators as appropriate.
• Contribute to the intellectual and cultural development of a growing interdisciplinary research lab.

  Qualifications

  Required:

  • Ph.D. (completed or expected) in a quantitative or biomedical discipline. Examples of quantitative disciplines: Mathematics, Statistics, Physics, Computer Science, Electrical Engineering, Computational Biology, Bioinformatics, Biostatistics, Systems Biology, and Statistical Genetics.
  • Proficiency in at least one programming language, such as Python, R, Julia, and MATLAB.
  • Strong interest in biological networks, causal inference, systems modeling, or reverse engineering of complex systems.
  • Ability to work both independently and collaboratively.
  • Track record of peer-reviewed publications.
  • Strong motivation, curiosity, and scientific rigor.
  • Biomedical background NOT required.

    Preferred:

    • Experience in network inference, causal inference, network science, dynamical systems, or systems science.
    • Experience in computational, statistical, or machine learning method development in any discipline.
    • Experience in germline or somatic genetic variations, neuroscience, microbiology, or another biological area relevant to the networks being studied.
    • Experience in analyzing single-cell, spatial, bulk sequencing, or other biological data.
    • Experience in responsible use of AI-assisted tools for computational research and software development.
    • Experience in algorithm design and good software development practices.
    • Effective communication skills.

      About the Principal Investigator

      Dr. Lingfei Wang is an Assistant Professor in the Department of Genomics and Computational Biology at UMass Chan Medical School. After completing a Ph.D. in theoretical physics, Dr. Wang transitioned into computational biology, with a research focus on causal inference of GRNs. His major contributions include:

      • Airqtl: the first method to map all eQTL candidates and infer cell state-specific causal GRNs from population-scale scRNA-seq datasets.
      • Dictys: the first method to dissect dynamic GRN rewiring from scRNA-seq+scATAC-seq data.
      • Normalisr: the first method to infer causal GRNs from Perturb-seq/single-cell CRISPR screen data.

        About the Lab

        Our computational lab was founded in October 2023 and develops new methods for inferring and analyzing causal GRNs from single-cell multi-omic data. The lab has protected access to several population-scale single-cell datasets derived from human blood and brain tissues. We are also expanding collaborations with experimental laboratories by building on the computational methods, software, and analytical capabilities developed within the lab.

        We believe that methodological advances in one research area can create opportunities in others. We are therefore broadening our interests beyond GRNs to other biological causal networks, such as neuronal and microbial interaction networks. Candidates with relevant expertise or new ideas are encouraged to help shape these emerging research directions.

        As an integral member of a growing lab, the postdoctoral researcher will benefit from:

        • High research independence and intellectual ownership.
        • Availability for frequent scientific discussion and rapid iteration of ideas.
        • Support for independent fellowship and grant applications.
        • Opportunities to gain mentoring, supervision, and teaching experience.
        • Conference participation and professional networking.
        • Hybrid-work flexibility.

          We particularly welcome applications from researchers representing diverse disciplines, cultures, countries, identities, underrepresented groups, and disadvantaged backgrounds.

          About the Department and the School

          The Department of Genomics and Computational Biology provides a highly collaborative environment for research at the intersection of computational biology, genomics, evolution, and human health. Faculty investigate gene regulation, genetics and epigenetics, evolutionary mechanisms, disease susceptibility, and treatment response through computational, statistical, and experimental approaches.

          UMass Chan Medical School is located in Worcester, Massachusetts. Researchers benefit from collaborations across UMass Chan, the broader University of Massachusetts system, nearby Worcester Polytechnic Institute, and research institutions throughout the Boston area. Boston is approximately one hour from Worcester by car and is also accessible by hourly train service, offering a broad range of academic, cultural, and recreational activities.

          Application Process

          To apply, submit your initial application as a single PDF to Lingfei.Wang@umassmed.edu. It should include:

          1. Cover letter describing your background, career goals, and why you are interested in this position.
          2. CV including a list of publications.
          3. Contact details for one to three references.
          4. Optional: Up to two representative publications or preprints.
          5. Optional: Additional supporting documents at your choice (e.g., code samples, public repositories, thesis copy).

             Applications are considered on a rolling basis. Typically you will be notified within two weeks if your application moves into the next stage.

             This position is immediately available and initially funded for two years, with possibility for renewal. All UMass Chan Medical School postdoc salaries follow the NIH stipend levels.

             Key papers

             • Airqtl dissects cell state-specific causal gene regulatory networks with efficient single-cell eQTL mapping. Matthew W. Funk, Yuhe Wang, and Lingfei Wang. Nature Communications 16 (2025), 11403.
             • Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multi-omics. Lingfei Wang et al. Nature Methods 20 (2023), 1368.
             • Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analysis with Normalisr. Lingfei Wang. Nature Communications 12 (2021), 6395.

               We look forward to your application!