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Assistant Professor Computational Biology Jobs (NOW HIRING)

The University Of Chicago

Job Summary The University of Chicago's Computational Oncology Unit seeks a Computational Biology Sr. Analyst to assist the Computational Oncology Core with biological data analysis. The Sr. Analyst ...

University of Idaho

Regular Faculty

Moscow, ID · On-site

... Rank Assistant Professor Extension Type Not Applicable Administrative Appointment Type Not ... Faculty also participate in the Bioinformatics and Computational Biology interdisciplinary graduate ...

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Assistant Professor Computational Biology information

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How much do assistant professor computational biology jobs pay per year?

As of Sep 12, 2026, the average yearly pay for assistant professor computational biology in the United States is $80,057.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,000.00 and $91,500.00 per year, depending on experience, location, and employer.

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Infographic showing various Assistant Professor Computational Biology job openings in the United States as of September 2026, with employment types broken down into 89% Full Time, and 11% Part Time. Highlights an 100% In-person job distribution, with an average salary of $80,057 per year, or $38.5 per hour.

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

Manhattan, NY • On-site

NYU Langone Health
Hospitals • 501 - 1,000 employees
Learn more about NYU Langone Healthexternal

Full-time

Re-posted 15 days ago

NYU Langone Health rating

8.4

Company rating: 8.4 out of 10

Based on 250 frontline employees who took The Breakroom Quiz

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.

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