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Postdoctoral Fellow
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks Full-time
We invite applications for a NIH-funded postdoctoral researcher position in our computational lab 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 dimensional complex systems. Our interdisciplinary approach integrates interpretable machine learning, statistics, algorithms, and single-cell multi-omics. We invite applicants from all quantitative fields and do not need prior biomedical knowledge.
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 novel 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 scientific rigor.
- 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:
- Airqtl: First method to map all eQTL candidates and infer cell state-specific causal GRNs from population-scale scRNA-seq datasets.
- Dictys: First method to dissect dynamic GRN rewiring from scRNA-seq+scATAC-seq data.
- Normalisr: 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 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
To apply, submit the following as a single PDF to [Please click the Apply button for the link or address]:
- 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 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!