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 based in New York and the selected candidate will be expected to work onsite as of their effective start date.

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 uses both experimental and computational genomics approaches to study gene regulation at the systems level. Planned projects for this position will focus on one or both of the following areas:

(1) Developing novel assays to measure variation in transcription factor - DNA binding across natural genetic backgrounds, and applying AI/ML models to predict cis-regulatory elements in plant genomes and uncover mechanistic insights into protein-DNA interactions.

(2) Developing and applying computational methods to infer gene regulatory networks from single-cell genomics data, including RNA-seq and ATAC-seq, and to predict phenotypic outcomes of genetic perturbations.

Our work primarily uses the model plant Arabidopsis. Funded projects are supported by NIH grants focused on mechanisms of transcription, as well as collaborative NSF and DOE grants that extend this work to agricultural and bioenergy crops.

In compliance with NYC’s Pay Transparency Act, the annual base salary range for this position is $72,000-$75,000. 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, biophysics, systems biology, bioinformatics, genetics/genomics, and/or plant biology, a track record or first- or co-first-author peer-reviewed publications, and prior research experience in any of these areas: molecular cloning, confocal microscopy, biophysical modeling of transcription, gene regulatory network inference and modeling, 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 and continued funding.

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.