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: Flexible. Immediate start available, with a preferred start date anytime between now and October 2026.
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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