美国马萨诸塞大学博士后职位招聘–数学、统计学、物理学、计算机科学、电气工程、计算生物学、生物信息学、生物统计学和统计遗传学等定量领域

We are looking for motivated postdoctoral researchers to perform causal inference and reconstruct dynamic gene regulatory network rewiring on large-scale single-cell and spatiotemporal multiomic datasets in our new computational lab at the University of Massachusetts Chan Medical School.

Our research covers several aspects such as statistical modelling, computational acceleration, multi-modal data integration, and biological interpretation. You will interpret state-of-the-art biomedical data with the latest theoretical and computational developments in causal inference and single-cell multiomics, benefiting from extensive discussion opportunities and nearly a decade of experiences and fresh ideas in the field.

About the position

The responsibilities include:

• Develop accurate and efficient computational methods to infer dynamic causal gene regulatory networks across tens of thousands of genes from millions of cells in diverse single-cell and spatiotemporal multiomic experiments
• Design robust objective metrics to evaluate and iteratively improve these methods and to compare them with existing approaches
• Demonstrate the unique capacity of these methods in systematically generating novel biological insights not previously understood, from molecular and cellular to organismal and population scales
• Distill these methods into user-friendly software packages
• Disseminate these methods with written manuscripts and academic presentations

About you

We seek postdoctoral researchers with:

• PhD degree (or expected) in a quantitative field such as mathematics, statistics, physics, computer science, electrical engineering, computational biology, bioinformatics, biostatistics, and statistical genetics
• Proficient in at least one modern programming language such as Python, Julia, and R
• Strong interest in gene regulatory network or causal inference
• Ability to work independently and as part of a team
• A track record of publications in peer-reviewed journals
  – Biomedical background not required

The following is considered a plus:

• Experience in network inference, causal inference, network science, algorithm, genome-wide association studies, Mendelian randomization, and/or dynamical systems
• Experience in computational, statistical, or machine learning method development in any discipline
• Experience in GPU computing frameworks, such as pytorch
• Experience working with single-cell, bulk sequencing, or other biological data
• Good practice in software development
• Strong communication skills

About the Principal Investigator

Dr. Lingfei Wang is a tenure-track Assistant Professor at the Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School. He obtained his Ph.D. in theoretical physics and transitioned to focus on the causal inference of gene regulatory networks since his postdoctoral studies. His notable contributions include the development of Normalisr, a pioneering computational method to infer causal gene regulatory networks from single-cell CRISPR screens.

More recently, he developed Dictys, the first computational method to dissect dynamic gene regulatory network rewiring in continuous biological processes from single-cell multi-omics. This study also incorporates a novel causal inference framework that effectively accounts for feedback loops. The recent emergence of population-scale scRNA-seq studies also presents a unique opportunity to adapt his causal gene regulatory network method Findr from bulk genome-transcriptome variations to single cells.

About the Lab

Our computational lab was opened in October 2023, dedicated to developing novel methods for inferring and analyzing causal gene regulatory networks. We primarily use single-cell and spatiotemporal multiomic datasets to uncover causal gene regulations. Besides lab projects utilizing the latest data modalities and theoretical developments, members are also encouraged to pursue their own ideas within the lab’s research theme. The lab is committed to supporting career development and meeting individual needs, such as conference participation and hybrid working arrangements. We particularly welcome people 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.

About the application

Please submit the following documents 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 and our lab
• CV including a list of publications
• Contact details for up to two references
• Up to 2 representative publications or preprints, and your roles in these studies
• Optional additional attachments for supporting documents, such as code samples, public repositories, and a copy of thesis

Positions are initially funded for two years, with the possibility for renewal subject to funding availability and performance. All UMass Chan Medical School postdocs are compensated at the levels set by the National Institutes of Health.

Key publications

• 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.
• Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data. Lingfei Wang and Tom Michoel. PLoS Computational Biology 13.8 (2017): e1005703.