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Job description
The Skok Lab at NYU Grossman School of Medicine is seeking an experienced computational scientist tolead the development of computational approaches for single-molecule epigenomics and 3D genomebiology. Our research integrates Oxford Nanopore (nano-NOMe-seq) and PacBio long-read sequencing withHi-C/Hi-ChIP, single-cell multi-omics, and machine-learning approaches to investigate chromatin topology,nucleosome organization, and gene regulation.
This position provides an opportunity to lead computational strategy within a collaborative,multidisciplinary research program while developing innovative analytical methods and pursuingindependent research directions.
The successful candidate will:
Develop computational pipelines for long-read sequencing data, from raw signal processing to per-molecule methylation, chromatin accessibility, and chromatin-state analysis.
Apply statistical and machine-learning approaches to model nucleosome organization, CTCF/transcription factor binding, and RNA Polymerase II elongation.
Integrate nano-NOMe-seq, Hi-C/Micro-C, RNA-seq, and single-cell multiome datasets to investigatechromatin architecture and gene regulation.
Lead computational analyses for collaborative research projects.
Mentor master's students and contribute to computational training within the laboratory.
Develop and pursue independent computational research directions.
Appointment as a Non-Tenure-Track Assistant Professor or Senior Staff Scientist, commensurate with experience. The position is renewable, fully supported, and includes a competitive salary andcomprehensive benefits package.
Start Date: Immediate start is preferred.
Minimum Qualifications:
PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, or a related quantitativefield.
At least 5 years of postdoctoral or equivalent experience working with long-read or single-moleculesequencing data.
Strong programming skills (e.g., Python, R, Bash/Linux).
Experience with workflow automation tools such as Snakemake, Nextflow, or similar platforms.
Demonstrated expertise in computational epigenomics, statistical analysis, and machine learning.
A record of scientific innovation, leadership, and collaborative research.
Preferred Qualifications:
Experience with one or more of the following:
Modified-base calling tools (e.g., Remora, Megalodon, Tombo).
3D genome analysis, including Hi-C, Micro-C, or related technologies.
Machine-learning approaches for per-molecule feature extraction, clustering, predictive modeling,deep representation learning, changepoint detection, or generative modeling.
NYU Langone Health is an equal opportunity employer and committed to inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration.
Worksite address
new york, NY, 10261, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.