Publisher
source

Lingfei Wang

6 months ago

Postdoc in Single-Cell and Spatial Gene Regulatory Networks (PhD-level) UMass Chan Medical School in United States

Degree Level

PhD, Postdoc

Field of study

Computer Science

Funding

NIH-funded postdoctoral position initially funded for two years with possibility of renewal. Salary follows NIH stipend levels. The post is described as a postdoc opening; no tuition funding is relevant.

Deadline

Expired

Country flag

Country

United States

University

University of Massachusetts Chan Medical School

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Keywords

Computer Science
Machine Learning
Biomedical Engineering
Biology
Mathematics
Computational Biology
Systems Biology
Causal Inference
Statistics
Bioinformatic
Spatial Transcriptomic

About this position

UMass Chan Medical School is advertising a postdoctoral position in single-cell and spatial multi-omic gene regulatory networks in the lab of Lingfei Wang, Assistant Professor in the Department of Genomics and Computational Biology. The lab develops computational and statistical methods to reconstruct causal gene regulatory networks (GRNs) from large-scale single-cell and spatial datasets, with research spanning Perturb-seq, scRNA-seq, scATAC-seq, and population-scale single-cell data.

The position is ideal for candidates interested in computational biology, statistics, machine learning, bioinformatics, and systems biology, especially those who want to work on causal inference, network inference, and reverse engineering of molecular interactions. The lab emphasizes research independence, rapid iteration, interdisciplinary collaboration, and publication-oriented work.

Research themes include causal GRNs from Perturb-seq, dynamic GRNs from scRNA-seq + scATAC-seq, and cell state-specific causal GRNs from population-scale scRNA-seq. The post also mentions opportunities to work with single-cell and spatial multi-omic data and to develop software and publish peer-reviewed methods and biological insights.

Eligibility highlights: applicants should have a PhD obtained or expected in a quantitative or biomedical discipline such as mathematics, statistics, physics, computer science, electrical engineering, computational biology, bioinformatics, biostatistics, systems biology, or statistical genetics. Strong programming ability in Python, R, Julia, or Matlab is required, along with a strong interest in gene regulatory networks, causal inference, and scientific rigor. Biomedical background is not required. Preferred experience includes network science, dynamical systems, differential equations, machine learning method development, single-cell/spatial/bulk sequencing analysis, and good software practices.

Funding: the position is NIH-funded, initially for two years with the possibility of renewal. Salary follows NIH stipend levels. The post is based at UMass Chan Medical School in Worcester, Massachusetts, with a collaborative environment and access to shared research resources.

Application: submit a single PDF by email to [email protected] including a cover letter, CV with publications, and optional references, representative papers/preprints, and other supporting materials. The post says applicants are typically notified within two weeks if they move to the next stage. Start date is as soon as possible.

Funding details

NIH-funded postdoctoral position initially funded for two years with possibility of renewal. Salary follows NIH stipend levels. The post is described as a postdoc opening; no tuition funding is relevant.

What's required

PhD obtained or expected in a quantitative or biomedical discipline; examples include mathematics, statistics, physics, computer science, electrical engineering, computational biology, bioinformatics, biostatistics, systems biology, and statistical genetics. Applicants should be proficient in at least one programming language such as Python, R, Julia, or Matlab; have a strong interest in gene regulatory networks, causal inference, or system reverse engineering; be able to work independently and collaboratively; have a track record of peer-reviewed publications; and show strong motivation, curiosity, and scientific rigor. Biomedical background is not required. Preferred experience includes network inference, causal inference, network science, dynamical systems, systems biology, ordinary/stochastic differential equations, computational/statistical/machine learning method development, single-cell or spatial data analysis, algorithms, software development, and effective communication skills.

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Professors

Assistant Professor

University Name
Country flag

United States

Research Interests

Cell Biology

Biochemistry

Systems Biology

Transcriptional Regulation

Single-cell Analysis