Postdoc in Biological Causal Networks

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.

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.

• 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.

• 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.

• 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.

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.

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…