Postdoc in Single-Cell and Spatial Multi-Omic Gene Regulatory Networks

Job Title: Postdoc in Single-Cell and Spatial Multi-Omic Gene Regulatory Networks

Job Number: 125576

Location: Worcester, US

We invite applications for a NIH-funded postdoctoral researcher position in the UMass Chan Medical School. Our lab specializes in reconstructing multi-omic 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.

You will develop and apply cutting-edge statistical models and computational methods to systematically extract knowledge of molecular interactions from single-cell and spatial multi-omic 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.

• Develop accurate and efficient computational methods to infer single-cell and spatial multi-omic causal GRNs across millions of cells and tens of thousands of genes.
• Apply these methods on existing and new datasets to generate novel biological insights at molecular, cellular, organismal, and population scales.
• Disseminate findings through peer-reviewed publications, user-friendly software packages, and academic presentations.
• Collaborate with other lab members and labs as needed.

• D.(obtained or expected) in a quantitative or biomedical discipline. Examples:
  Mathematics, Statistics, Physics, Computer Science, Electrical Engineering, Computational Biology, Bioinformatics, Biostatistics, Systems Biology, Statistical Genetics.
• Proficiency in at least one programming language such as Python, R, Julia, or Matlab.
• 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.

• Experience in network inference, causal inference, network science, dynamical systems, systems science (e.g. systems biology), or ordinary/stochastic differential equations.
• Experience in computational, statistical, or machine learning method development in any discipline.
• Experience in analyzing single-cell, spatial, bulk sequencing, or other biological data.
• Experience in algorithms and good software development practices.
• Effective communication skills.

Final date to receive applications: