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, condition-dependent 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 and implement 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).
• Design robust evaluation metrics and benchmarking pipelines for network inference methods.
• Demonstrate how these models uncover biological principles and insights across molecular, cellular, and population levels.
• Build open-source, user-friendly software tools for the community.
• Publish results in top peer-reviewed journals and conferences.

Qualifications

Required:

• Ph.D. (obtained or expected) in a quantitative discipline: Computer Science, Physics, Applied Mathematics, Statistics, Computational Biology, Bioinformatics, Systems Biology, or related fields.
• Proficiency in at least one programming language (e.g., Python, Julia, R, C/C++, or Fortran).
• Interest in network science, causal inference, or system identification.
• Track record of peer-reviewed publications.
• Ability to work independently and collaboratively.
• Curiosity, motivation, and scientific rigor.
• No biomedical background required.

Preferred:

• Experience in network inference, causal inference, network science, dynamical systems, systems science (e.g. systems biology), probabilistic modeling, or ordinary/stochastic differential equations.
• Experience in computational, statistical, or machine learning method development in any discipline.
• Experience in GPU computing frameworks (e.g. PyTorch).
• Experience analyzing high-dimensional data (biological or otherwise) or single-cell, bulk sequencing, or other biological data.
• Experience in algorithms 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. Trained as a theoretical physicist, he now develops methods for causal network inference in biology. Key contributions include:

• Airqtl: First method to map all eQTLs candidates and state-specific causal networks from population-scale scRNA-seq.
• Dictys: First method to dissect dynamic network rewiring from single-cell multi-omic data.
• Normalisr: First method to infer causal networks from Perturb-seq data.

About the Lab

Founded in October 2023, our lab develops algorithms to extract causal, interpretable networks from single-cell and spatiotemporal data. We value independent thinking, open science, and career development support (e.g. conference travel, hybrid work options, mentorship, funding application). We particularly welcome applicants from diverse disciplines, backgrounds, and perspectives.

About the Department

The Department of Genomics and Computational Biology at UMass Chan Medical School, located in the state-of-the-art Albert Sherman Center, is a forefront of research in Computational Biology, Evolutionary Biology, and Genomics. The Department focuses on deciphering complex biological data using computational and genomic methods. Key research areas include regulatory mechanisms in mammalian evolution, the interplay between genetics and epigenetics in human health, and genetic diversity in disease susceptibility and treatment responses. The Department is committed to an inclusive, collaborative environment, integrating with adjacent departments and benefiting from shared cutting-edge facilities. This synergy, along with advanced computing and experimental resources, propels the Department’s exploration of molecular, cellular, and evolutionary mechanisms in health and disease.

About the University

The UMass system includes UMass Chan Medical School and campuses at Amherst, Dartmouth, Lowell, and Boston. Collaborations thrive between UMass institutions and Worcester Polytechnic Institute (WPI), located within a 10-minute drive from UMass Chan. Joint research and educational initiatives flourish in genomics and computational biology. UMass Chan Medical School has been named one of The Boston Globe’s Top Places to Work in Massachusetts for two consecutive years. UMass Chan Medical School is located in Worcester with affordable housing and a vibrant community for over 30,000 college students at ten institutions of higher education. Boston is an hour drive away with numerous academic and recreational activities.

Application Process

Submit the following as a single PDF to the email contact at the bottom of this page:

1. Cover letter describing your background, career goals, and fit for this position.
2. CV including a list of publications.
3. Contact details for up to three references.
4. Up to two representative publications/preprints, with a description of your contribution.
5. Optional: Code samples, public repos, thesis, or other supporting materials.

This position is funded for three years (renewable), with salaries following 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. bioRxiv 2025.01.15.633041.
• 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!