Postdoctoral Associate (Huang Lab)

The Huang lab at NYU Biology is looking for a highly motivated and independent individual to work as a Postdoctoral Associate.

This position is for a post-PhD trainee preparing for a research career path in academia or industry. The planned position will provide a transition to career independence through the development of professional skills and research profiles; supervision by faculty mentor incorporating individual development plan in support of training goals and grant objectives; and publication of high-impact research findings/scholarship during postdoc appointment period.

The Huang lab use both experimental and computational genomics methods to study gene regulation at the systems level. Planned projects for this position will focus on one or both of two major directions: (1) developing and applying computational methods to infer gene regulatory networks from single cell genomics data (RNA-seq, ATAC-seq) and to predict phenotypic outcomes of genetic perturbations; (2) developing and applying genomic language models to predict cis-regulatory elements in plant genomes and to investigate the mechanisms of protein-DNA interactions. We primarily work with the model plant Arabidopsis, and our funded projects are supported by collaborative NSF and DOE (Department of Energy) grants that extend to agricultural and bioenergy crops.

In compliance with NYC’s Pay Transparency Act, the annual base salary range for this position is $67,000-$70,000. New York University considers factors such as (but not limited to) the specific grant funding and the terms of the research grant when extending an offer.

The ideal candidate will hold a PhD in computational biology, systems biology, bioinformatics, genetics/genomics, plant biology, and/or ecology/evolution, a track record or first- or co-first-author peer-reviewed publications, and prior research experience in any of these areas: analysis of single cell genomics data, gene regulatory network inference and modeling, comparative genomics, protein and DNA language models, machine learning and deep learning. Experience in working with Arabidopsis and plant genome data is a strong plus. The position is expected to continue for multiple years contingent on satisfactory performance.

Please upload your application materials via Interfolio. Include the following items: 1) CV including a list of publications; 2) a short summary of your present and future research interests; 3) a list of three references and their contact information.