Senior Computational Biologist / Non-Tenure-Track Assistant Professor / Faculty Research Scientist

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.

Qualifications

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.

Application Instructions

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