Computational Biologist / Postdoctoral Fellow - Epigenomics, Machine Learning & 3D Genome Biology

The Skok Lab at NYU Grossman School of Medicine is seeking a computational scientist to join our multidisciplinary research program studying chromatin organization, epigenomics, and gene regulation. We are recruiting at either the Postdoctoral Fellow or Senior Computational Biologist / Non-Tenure-Track Assistant Professor level, depending on experience and qualifications. Our research integrates Oxford Nanopore and PacBio long-read sequencing, nano-NOMe-seq, Hi-C/Hi-ChIP, single-cell multi-omics, RNA-seq, and machine-learning approaches to investigate chromatin topology, nucleosome organization, transcription factor binding, and gene regulation. This position provides an opportunity to work at the intersection of computational biology, genomics, epigenomics, and chromatin biology within a collaborative and multidisciplinary research environment. Candidates will contribute to ongoing research while developing and pursuing independent computational research directions. Research Focus Develop and apply computational pipelines for long-read and single-molecule sequencing data. Analyze per-molecule DNA methylation, chromatin accessibility, and chromatin-state information. Apply statistical and machine-learning approaches to study 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 investigate chromatin architecture and gene regulation. Develop reproducible computational workflows using Snakemake, Nextflow, or similar platforms. Lead or contribute to computational analyses for collaborative research projects. Collaborate closely with experimental scientists to integrate computational and molecular data. Present research findings at lab meetings, conferences, and scientific publications. Develop independent computational research questions and research directions. Qualifications Required PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, Biology, or a related quantitative or biological field. Strong programming skills in one or more of Python, R, and Bash/Linux. Experience with bioinformatics, genomics, computational biology, or related quantitative analyses. Demonstrated ability to analyze and interpret complex biological datasets. Strong interest or experience in computational epigenomics, chromatin biology, genomics, or machine learning. Ability to work independently while collaborating effectively in a multidisciplinary research environment. Strong written and oral communication skills. Preferred Experience Oxford Nanopore or PacBio long-read sequencing. Modified-base calling and analysis tools such as Remora, Megalodon, or Tombo. nano-NOMe-seq or related methods for simultaneous analysis of DNA methylation and chromatin accessibility. 3D genome analysis, including Hi-C, Micro-C, or Hi-ChIP. RNA-seq or single-cell multi-omics. Machine-learning approaches including feature extraction, clustering, predictive modeling, deep representation learning, changepoint detection, or generative modeling. Workflow automation using Snakemake, Nextflow, or similar platforms. HPC or cloud computing environments. Long-read or single-molecule sequencing, computational epigenomics, and/or 3D genome analysis. Statistical and machine-learning approaches for biological data. Leadership of computational research projects and collaborative scientific programs. Development of innovative computational methods and independent research directions. Research Environment Join a multidisciplinary research program integrating molecular biology, chromatin biochemistry, genomics, and computation. The Skok Lab provides access to high-throughput Oxford Nanopore and PacBio sequencing, HPC/cloud computational resources, and NIH-funded collaborative research consortia. The successful candidate will work closely with experimental and computational scientists and have opportunities to develop expertise in emerging sequencing technologies, computational epigenomics, and machine learning while contributing to cutting-edge studies of chromatin organization and gene regulation. Appointment and Career Development Appointment will be at either the Postdoctoral Fellow or Senior Computational Biologist / Non-Tenure-Track Assistant Professor level, commensurate with experience and qualifications. For postdoctoral candidates, the position provides strong training in computational genomics and epigenomics, opportunities to develop independent research projects, contribute to publications, and present research at national and international conferences. For senior candidates, the position provides an opportunity to lead computational strategy within the research program, mentor junior scientists, develop innovative analytical approaches, and pursue independent research directions. Appointment at the senior level may be as a Non-Tenure-Track Assistant Professor or Senior Staff Scientist, commensurate with experience. Both positions are fully supported and include a competitive salary and comprehensive benefits package. Start Date Flexible. Immediate start preferred. Keywords Oxford Nanopore PacBio nano-NOMe-seq 3D Genome Chromatin Nucleosome Cohesin Transcription Epigenomics Machine Learning Single-Molecule Sequencing Computational Biology Bioinformatics Long-Read Sequencing #J-18808-Ljbffr NYU Langone Health, Skok Lab