Postdoc in Biological Causal Networks

Position Description

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{margin-bottom:0cm;}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 the email contact at the bottom of this
page. 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!

As an equal opportunity and affirmative action employer, UMass Chan recognizes the added value of a diverse community and encourages applications from individuals with varied experiences, perspectives, and backgrounds.