University of Massachusetts Chan Medical School
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School, Worcester, Massachusetts, us, 01609
Job Title:
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks Job Number:
105237 Location:
Worcester,US Job Description We invite applications for a NIH-funded
postdoctoral researcher
position in [Please click the Apply button for the link or email] at
UMass Chan Medical School . Our lab specializes in reconstructing
multi-omic, multi-context causal gene regulatory networks (GRNs)
from large-scale
single-cell datasets . We are pioneers in GRN reconstruction from single-cell multi-omics, including: Causal GRNs
from Perturb-seq Dynamic GRNs
from scRNA-seq + scATAC-seq Cell state-specific causal GRNs
from population-scale scRNA-seq We continue to push the boundaries of
reverse-engineering molecular interactions
from observational and interventional datasets in high dimensions. Our interdisciplinary approach integrates
interpretable machine learning, statistics, algorithms, and single-cell multi-omics . Position Overview
You will develop cutting-edge
statistical models and computational methods
to systematically extract knowledge of molecular interactions from single-cell multi-omic, multi-context data. As an integral part of our young lab, you will benefit from opportunities of
high research independence, extensive discussions, and rapid iteration of tested ideas . If you are passionate about uncovering the
fundamental principles and intricate interactions within a high-dimensional system like gene regulation , join us! Key Responsibilities
Develop
accurate and efficient computational methods
to infer single-cell multi-omic, multi-context causal GRNs across
millions of cells and tens of thousands of genes . Design
robust objective metrics
for evaluating methods and benchmarking against existing approaches. Demonstrate the unique capacity of these methods to generate
new biological insights
at
molecular, cellular, organismal, and population scales . Distill these methods into
user-friendly software packages . Disseminate findings through
peer-reviewed publications and academic presentations . Qualifications
Required:
Ph.D. (obtained or expected) in a
quantitative field
such as 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, Julia, R, C, C++, or Fortran . Strong interest in
gene regulatory networks, causal inference, or system reverse engineering . Ability to work both
independently and collaboratively . Track record of
peer-reviewed publications . Strong motivation, curiosity, and high standards
for research. Biomedical background NOT required. Preferred:
Experience in
network inference, causal inference, network science, dynamical systems, systems science (e.g. systems biology), probabilistic programming, 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
single-cell, bulk sequencing, or other biological data . Experience in
algorithms
and good
software development practices . Good
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. With a Ph.D.in theoretical physics, his research transitioned to focus on
causal inference of GRNs . His key contributions include: :
First method to map all
eQTL
candidates and infer
cell state-specific causal GRNs
from population-scale scRNA-seq datasets. :
First method to
dissect dynamic GRN rewiring
from scRNA-seq+scATAC-seq data. :
First method to infer causal GRNs from
Perturb-seq/single-cell CRISPR screen About the Lab
Our lab, founded in
October 2023 , develops
novel computational methods
to infer and analyze causal GRNs using
single-cell and spatiotemporal multi-omic data . We encourage members to pursue independent ideas within our research theme and provide
career development support , such as conference participation, hybrid work flexibility, and career mentorship. We particularly welcome applications from
diverse disciplines, cultures, countries, underrepresented minority groups, and disadvantaged backgrounds . About the Department
The
[Please click the Apply button for the link or email]
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
[Please click the Apply button for the link or email]
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
[Please click the Apply button for the link or email]
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
To apply, submit the following as a
single PDF
to the listed application email: Cover letter
describing your background, career goals, and
why you are interested in this position . CV
including a list of publications. Contact details
for
up to three references . Up to two representative publications or preprints , with a description of your role in these studies. Optional:
Additional supporting documents at your choice (e.g., code samples, public repositories, thesis copy). This position is
funded for three years , with possibility for renewal. All UMass Chan Medical School postdoc salaries follow the
apply> . 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. . Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multi-omics. Lingfei Wang et al.. Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analysis with Normalisr. Lingfei Wang. . We look forward to your application! Application Deadline:
2025-10-31
#J-18808-Ljbffr
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks Job Number:
105237 Location:
Worcester,US Job Description We invite applications for a NIH-funded
postdoctoral researcher
position in [Please click the Apply button for the link or email] at
UMass Chan Medical School . Our lab specializes in reconstructing
multi-omic, multi-context causal gene regulatory networks (GRNs)
from large-scale
single-cell datasets . We are pioneers in GRN reconstruction from single-cell multi-omics, including: Causal GRNs
from Perturb-seq Dynamic GRNs
from scRNA-seq + scATAC-seq Cell state-specific causal GRNs
from population-scale scRNA-seq We continue to push the boundaries of
reverse-engineering molecular interactions
from observational and interventional datasets in high dimensions. Our interdisciplinary approach integrates
interpretable machine learning, statistics, algorithms, and single-cell multi-omics . Position Overview
You will develop cutting-edge
statistical models and computational methods
to systematically extract knowledge of molecular interactions from single-cell multi-omic, multi-context data. As an integral part of our young lab, you will benefit from opportunities of
high research independence, extensive discussions, and rapid iteration of tested ideas . If you are passionate about uncovering the
fundamental principles and intricate interactions within a high-dimensional system like gene regulation , join us! Key Responsibilities
Develop
accurate and efficient computational methods
to infer single-cell multi-omic, multi-context causal GRNs across
millions of cells and tens of thousands of genes . Design
robust objective metrics
for evaluating methods and benchmarking against existing approaches. Demonstrate the unique capacity of these methods to generate
new biological insights
at
molecular, cellular, organismal, and population scales . Distill these methods into
user-friendly software packages . Disseminate findings through
peer-reviewed publications and academic presentations . Qualifications
Required:
Ph.D. (obtained or expected) in a
quantitative field
such as 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, Julia, R, C, C++, or Fortran . Strong interest in
gene regulatory networks, causal inference, or system reverse engineering . Ability to work both
independently and collaboratively . Track record of
peer-reviewed publications . Strong motivation, curiosity, and high standards
for research. Biomedical background NOT required. Preferred:
Experience in
network inference, causal inference, network science, dynamical systems, systems science (e.g. systems biology), probabilistic programming, 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
single-cell, bulk sequencing, or other biological data . Experience in
algorithms
and good
software development practices . Good
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. With a Ph.D.in theoretical physics, his research transitioned to focus on
causal inference of GRNs . His key contributions include: :
First method to map all
eQTL
candidates and infer
cell state-specific causal GRNs
from population-scale scRNA-seq datasets. :
First method to
dissect dynamic GRN rewiring
from scRNA-seq+scATAC-seq data. :
First method to infer causal GRNs from
Perturb-seq/single-cell CRISPR screen About the Lab
Our lab, founded in
October 2023 , develops
novel computational methods
to infer and analyze causal GRNs using
single-cell and spatiotemporal multi-omic data . We encourage members to pursue independent ideas within our research theme and provide
career development support , such as conference participation, hybrid work flexibility, and career mentorship. We particularly welcome applications from
diverse disciplines, cultures, countries, underrepresented minority groups, and disadvantaged backgrounds . About the Department
The
[Please click the Apply button for the link or email]
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
[Please click the Apply button for the link or email]
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
[Please click the Apply button for the link or email]
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
To apply, submit the following as a
single PDF
to the listed application email: Cover letter
describing your background, career goals, and
why you are interested in this position . CV
including a list of publications. Contact details
for
up to three references . Up to two representative publications or preprints , with a description of your role in these studies. Optional:
Additional supporting documents at your choice (e.g., code samples, public repositories, thesis copy). This position is
funded for three years , with possibility for renewal. All UMass Chan Medical School postdoc salaries follow the
apply> . 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. . Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multi-omics. Lingfei Wang et al.. Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analysis with Normalisr. Lingfei Wang. . We look forward to your application! Application Deadline:
2025-10-31
#J-18808-Ljbffr